research project on liver disease

Scientists Discover a New Signaling Pathway and Design a Novel Drug for Liver Fibrosis

Their research could lead to treatment for a variety of liver diseases and conditions..

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• Liver fibrosis
• Fatty liver disease

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A healthy liver filters all the blood in your body, breaks down toxins and digests fats. It produces collagen to repair damaged cells when the liver is injured. However, a liver can produce too much collagen when an excess accumulation of fat causes chronic inflammation, a condition called metabolic dysfunction-associated steatohepatitis (MASH). In an advanced state, MASH can lead to cirrhosis, liver cancer and liver-related death.  

The cells that produce collagen in livers are called hepatic stellate cells (HSC). In a new paper published in Cell Metabolism , scientists from the University of California San Diego investigated how these cells are activated. They found a three-component signaling pathway in the nucleus that functions according to a sort of police-controlling-the-police model.

In healthy livers, the first molecule in the pathway inhibits a second molecule, which inhibits the molecule that stimulates collagen-producing genes. MASH scales down the first molecule, so that inhibition is lifted on the second and third molecules, leading to stimulation of collagen production.

After discovering the signaling pathway, the scientists designed a short piece of RNA to prove the pathway behaved the way they thought it did. This RNA, called an antisense oligonucleotide (ASO), was so effective that it not only proved the viability of the pathway but also prevented liver fibrosis — too much collagen in the liver — without causing any side effects. The scientists are currently discussing licensing the ASO as a therapeutic with various pharmaceutical and biotech companies.

“All the molecules in the pathway were known, but no one knew if or how they interacted. We put the pathway together, showing each step in this intracellular signaling module. That was the science of the research. The clinical message is this ASO, which can actually block liver fibrosis,” said Jerrold Olefsky, a professor of medicine and assistant vice chancellor for integrative research at UC San Diego Health Sciences, and senior author on the paper.

The scientists carried out their investigations in organoids — tiny little livers in a dish made from three types of liver cells fed a MASH cocktail of fatty acids, fructose and sugars. They found that in normal livers, the first component of the pathway, a nuclear seven-transmembrane protein called TM7SF3, inhibits a splicing factor called hnRNPU. hnRNPU refrains from splicing out an inhibitory exon in the messenger RNA (mRNA) of TEAD1, a transcription factor that controls the genes that produce collagen. The inhibitory exon, exon 5, keeps TEAD1 from turning on the collagen-producing genes.

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Schematic of the TEAD 1 signaliing module that drives liver fibrosis. Photo by UC San Diego Health Sciences

In the MASH-fed organoids, TM7SF3 is reduced and does not inhibit the splicing factor. The active splicing factor splices out the inhibitory exon in the transcription factor, which turns on the genes that produce collagen. This is called alternative splicing. 

Furthering their investigations, they designed an ASO to keep hnRNPU from splicing out exon 5. “It had the sequence that would put it just upstream of exon 5 where hnRPU binds. The ASO prevented the splicing factor from binding to the TEAD mRNA, so it couldn’t ultimately splice it out. We got pretty much only inactive TEAD when we treated MASH mice with the ASO,” said Olefsky. With TEAD inactivated, collagen wasn’t produced. Neither was fibrosis.

“These findings show the key role of alternative splicing in shaping progression of fibrotic liver disease,” said Roi Isaac, Ph.D., assistant project scientist, Medicine, and first author on the paper.

When ASOs are administered intravenously, they enter every cell in the body, not just the target cells. Serendipitously, the scientists found that within the liver, this hnRNPU mechanism only operated in stellate cells. That made their ASO both highly effective and highly specific — the epitome of good drug design. 

According to Olefsky, a quarter of the U.S. population has MASH. While the scientists’ ASO may well be an effective treatment, getting FDA approval for it would necessitate enormous clinical studies and could cost up to a billion dollars. 

Olefsky and his team identified a much less common disease called primary sclerosing cholangitis, or PSC. It's relatively rare, often fatal, and until now there were no good treatments.  When the scientists tested their ASO on a mouse model of PSC, they found that their ASO almost completely blocked the development of the disease. “Getting FDA approval for PSC would be easier than getting it for MASH,” said Olefsky, “so we’re currently talking to biotech and pharma partners about licensing the ASO for PSC.” 

Additional authors are: Gautam Bandyopadhyay, Theresa V. Rohm, Sion Kang, Jinyue Wang, Narayan Pokhre, Sadatsugu Sakane, Rizaldy Zapata, Avraham M. Libster, Asres Berhan,Tatiana Kisseleva, Zea Borok, Francesca Telese, Nicholas JG Webster of the University of California San Diego, and Varon Vinik and Yehiel Zick of the Weizmann Institute of Science, Israel.

This study was funded by the National Institutes of Health, the Swiss National Science Foundation, the Larry L. Hillblom Foundation, and Janssen Pharmaceuticals.

Disclosures: Roi Isaac and Jerrold Olefsky are co-inventors on a provisional patent for the use of ASO 56 as an inhibitor of liver fibrosis.

“All the molecules in the pathway were known, but no one knew if or how they interacted. We put the pathway together. That was the science of the research. The clinical message is this ASO, which can actually block liver fibrosis.”

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Liver Disease

The liver performs many critical metabolic functions, including processing and distribution of nutrients. Liver diseases can be caused by infection, such as hepatitis B and C, or by genetic mutations. Other liver diseases can be triggered by autoimmune reactions or drug toxicity. The rise in obesity in the United States has led to a rise in nonalcoholic fatty liver disease. Many liver diseases place individuals at higher risk for developing liver cancer.

The only current treatment for end-stage liver disease is a liver transplant, and the number of livers available from deceased donors is limited. Thus NIDDK-supported liver research focuses on identifying liver disease early, preserving liver function in people with liver disease, and developing new treatment options, including transplants performed with liver tissue from living donors.

Other NIDDK-funded research is investigating the role gut microbes may play in the progression of nonalcoholic fatty liver disease, and in understanding how the body’s natural killer T cells can activate an immune response to hepatitis B.

In a collaborative effort with the National Library of Medicine, NIDDK has developed LiverTox , an online resource for drug-induced liver injury, providing a “living textbook” with hundreds of case reports, patient information, and a database of over a thousand drugs and supplements.

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Artificial Liver and Liver Transplantation: Scott L. Nyberg

Research projects.

Dr. Nyberg leads several ongoing research projects in the Artificial Liver and Liver Transplantation Lab, including efforts to develop an artificial liver and to advance liver regeneration. Read more about our work.

Human liver cell expansion

Hepatitis, cirrhosis, liver cancer, including hepatocellular carcinoma, and other liver diseases affect millions of people around the world. Research on liver disease lags behind other well-studied, prominent diseases because of a lack of appropriate research models.

Our lab is working to overcome this research obstacle. To address the need for a large animal model of hepatocellular carcinoma arising spontaneously within a background of regenerative nodules and cirrhosis, Dr. Nyberg led the development of the first porcine model of hereditary tyrosinemia type 1 (HT1).

Hereditary tyrosinemia type 1 is a liver disease caused by a deficiency in the enzyme fumarylacetoacetate hydrolase (FAH). This disease results in hepatic failure, cirrhosis and liver cancer early in childhood.

The FAH -deficient porcine model was created by targeting and disrupting the porcine FAH gene. The model is phenotypically normal but has decreased FAH transcriptional and enzymatic activity compared with wild-type animals. Our lab is working to use the new model for gene and cell therapy research, especially for studies related to hepatocyte and bone marrow transplantation.

We also envision the HT1 model as an in vivo incubator to grow liver cells, called hepatocytes, from humans, something previously done using FAH mutant mice. The hepatocytes generated in the mice can fully function as adult primary hepatocytes able to perform all the necessary functions required in a human liver. Replicating this success in the larger porcine model is highly desirable since large numbers of human cells are needed for extracorporeal liver assist devices, such as the bioartificial liver. The HT1 model also may be used to grow patient-specific hepatocytes for cell transplantation, avoiding the need for immunosuppression after transplantation.

Related publications:

• Scott L. Nyberg, et al. Curative ex vivo liver-directed gene therapy in a pig model of hereditary tyrosinemia type 1 . Science Translational Medicine. 2016; doi:10.1126/scitranslmed.aaf3838.
• Scott L. Nyberg, et al. Fumarylacetoacetate hydrolase gene as a knockout target for hepatic chimerism and donor liver production . Stem Cell Reports. 2021; doi:10.1016/j.stemcr.2021.09.018.

Bioartificial livers

Our lab has developed a novel device called the Spheroid Reservoir Bioartificial Liver (SRBAL). This device is being evaluated in a preclinical model of liver failure. The Spheroid Reservoir Bioartificial Liver can act as a potential bridge to liver transplantation. Or it may provide temporary support until spontaneous recovery of the liver. Our ultimate goal is to use this artificial liver device to treat people experiencing liver failure, such as liver failure after a drug overdose.

• Scott L. Nyberg, et al. Pivotal preclinical trial of the spheroid reservoir bioartificial liver . Journal of Hepatology. 2015; doi:10.1016/j.jhep.2015.03.021.

Bioengineered replacement livers

In collaboration with industry partner Miromatrix Medical Inc., our lab is developing a protocol for producing transplantable human livers from decellularized pig liver models.

This process involves removing all the liver cells from the model using a continuous perfusion technique. This results in a decellularized bio-scaffold with the original architecture, mechanical properties and a vascular network of a normal liver. The bio-scaffold is recellularized with cell types necessary to restore normal function and blood flow. Our goal is to create a bioengineered replacement liver enabling a large animal model to recover in the absence of native function and survive three months with a normal lifestyle. This milestone is required before considering clinical application and U.S. Food and Drug Administration approval.

• Scott L. Nyberg, et al. Sustained perfusion of revascularized bioengineered livers heterotopically transplanted into immunosuppressed pigs . Nature Biomedical Engineering. 2020; doi:10.1038/s41551-019-0460-x.

Liver regeneration

Our lab is working to establish new therapies to replace liver transplantation. One alternative is the extracorporeal bioartificial liver device, which has shown a survival benefit and neuroprotective effect in three preclinical large animal models.

Despite these advances, a pharmacological approach to induce liver regeneration would be less invasive and logistically superior to extracorporeal therapy. Indeed, strategies for producing druggable biological targets to stimulate liver regeneration are of therapeutic value and could offer survival benefit. Striving for this goal, RNA interference (RNAi) screening of human tumor cell lines led to the successful identification of the gene mitogen-activated protein kinase kinase 4 (MKK4), whose inhibition leads to a robust elevation in the regenerative capacity of cultured hepatocytes.

To confirm these results in a large animal model, the pig was selected because its liver resembles the human liver in size and function. In addition, the pig hepatectomy model develops signs similar to people with acute liver failure. The model also enables measurement of clinically relevant functional parameters and monitoring of liver size and regeneration using imaging modalities.

Dr. Nyberg's team reported the first randomized preclinical study of LN3348, a novel hepato-regenerative MKK4 inhibitor drug, in a porcine model of post-hepatectomy acute liver failure. Regeneration of the remnant liver was quantified by computerized tomography volumetrics and Ki-67 immunohistochemistry staining. Dr. Nyberg's lab team also observed that LN3348 promoted liver regeneration and improved survival in the porcine hepatectomy model. Clinical testing of this drug is underway. Our lab is continuing to study this new drug and related drugs in other large animal models of liver disease.

• Scott L. Nyberg, et al. Randomized trial of spheroid reservoir bioartificial liver in porcine model of posthepatectomy liver failure . Hepatology. 2019; doi:10.1002/hep.30184.

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Study identifies driver of liver cancer that could be target for treatment.

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Metabolic diseases like obesity can increase the risk of developing liver cancer, research has shown. But how one disease predisposes to the other is unclear. In a new study, Yale researchers uncovered a key role played by a molecule called fatty acid binding protein 5 (FABP5) and found that inhibiting it blocked tumor progression in many cases.

The molecule, said the researchers, could be a target for cancer treatment in the future.

The findings were published April 25 in Nature Metabolism.

Hepatocellular carcinoma is a type of cancer that accounts for 90% of liver tumors and it’s the second-leading cause of cancer-related deaths worldwide.

“ Obesity-related hepatocellular carcinoma is also on the rise in the United States as rates of metabolic disease increase,” said Carlos Fernández-Hernando , the Anthony N. Brady Professor of Comparative Medicine and professor of pathology at Yale School of Medicine (YSM) and senior author of the study.

Obesity can lead to non-alcoholic fatty liver disease, in which excess fat builds up in the liver. In some people, this disease transitions to a more inflammatory condition called non-alcoholic steatohepatitis, Fernández-Hernando explained, and this can lead to liver cancer.

To study what might be driving this disease transition, the researchers fed mice a specific diet that induces fat accumulation in the liver. Previous studies have shown that this diet over time induces non-alcoholic fatty liver disease, followed by non-alcoholic steatohepatitis, and then hepatocellular carcinoma in mice, mimicking the disease transition in humans.

While the mice were on this diet, the researchers looked for any changes in gene expression across various liver cells.

“ One thing that stood out to us was that this molecule FABP5 was highly elevated in liver tumor cells,” said Jonathan Sun, a Ph.D. student in Fernández-Hernando’s lab and lead author of the study. “We also observed that it was expressed in immune cells called macrophages localized in the tumors.”

Depending on the context and the cell type, FABP5 can perform different roles, but ultimately, it’s a molecule that binds to fatty acids and moves them around a cell. Supporting its potential relevance in humans, using data from the Cancer Genome Atlas — a collection of more than 20,000 human cancer samples — the researchers found that FABP5 was overexpressed in human hepatocellular carcinoma cells and patients with high expression of FABP5 had a significantly lower five-year survival rate than patients with low FABP5 expression.

“ Together, these findings told us that inhibiting FABP5 might be a good target for treating tumor progression,” said Yajaira Suárez , the Anthony N. Brady Professor of Comparative Medicine and professor of pathology at YSM and co-senior author of the study.

After treating a subset of mice with a molecule that inhibits FABP5, the researchers found that it blocked tumor progression. While 50% of the mice not treated with inhibitor went on to develop liver tumors, just 25% of the mice treated with the inhibitor did. The tumors they did develop were also fewer in number and smaller than those of their untreated counterparts. These findings were further substantiated in mice genetically deficient in FABP5, which were significantly resistant to obesity-driven hepatocellular carcinoma.

The researchers found two potential explanations for why inhibiting FABP5 had this effect on tumors: Inhibiting FABP5 made the tumor cells more susceptible to a cell death called ferroptosis, and it changed the tumor microenvironment.

“ Inhibiting FABP5 caused macrophages to shift to a more pro-inflammatory state that led them to activate other immune cells,” said Sun. “It rewired the microenvironment to be more aggressive against cancer cells.”

The findings are promising when it comes to potential treatments for liver cancer in humans, said Fernández-Hernando, who is also a member of the Yale Cancer Center and director of the Vascular Biology and Therapeutics Program at YSM. Going forward, he and his lab aim to better understand the link between FABP5 and ferroptosis at the molecular level and test how FABP5 inhibition might affect other cancers and illnesses like cardiovascular disease.

Collaborators of the study include the laboratory of Rachel Perry at YSM, the laboratories of Iwao Ojima and Martin Kaczocha at Stony Brook University, and members of the Liver Center at YSM.

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• v.28(40); 2022 Oct 28

Management of liver diseases: Current perspectives

Gastroenterology Unit, Department of Medicine, B.R.Singh (Railway) Hospital, Kolkata 700014, West Bengal, India. moc.oohay@0191matuag

Corresponding author: Gautam Ray, DNB, MD, Academic Research, Chief Doctor, Doctor, Teacher, Gastroenterology Unit, Department of Medicine, B.R.Singh (Railway) Hospital, Parikshit Roy Lane, Near Sealdah Bus Stop, Beliaghata, Kolkata 700014, West Bengal, India. moc.oohay@0191matuag

There is increasing incidence and prevalence of acute and chronic liver diseases (CLDs) all over the world which influence the quality of life and can give rise to life threatening complications. The burden of advanced liver disease due to hepatitis B has been controlled by antivirals but its eradication is difficult soon. Highly effective directly acting antiviral therapy has reduced the burden of hepatitis C but is partially offset by increasing IV drug abuse. Non-alcoholic fatty liver disease pandemic is on and there is recent alarming increase in alcohol related liver disease, both of which have no drug cure apart from control of the risk factors. Genetic factors have been identified in progression of all forms of CLD. Due to better management of complications of CLD, the life span of patients have increased spiking the number of hepatocellular carcinoma (HCC) and patients needing liver transplantation (LT). The present severe acute respiratory syndrome coronavirus pandemic has affected the outcome CLD including LT in addition to causing acute hepatitis. Better diagnostics and therapeutics are available for liver fibrosis, portal hypertension, HCC and post LT management and many drugs are under trial. The present review summarises the current scenario of the epidemiology and the advances in diagnosis and treatment of liver diseases including their complications like portal hypertension, HCC and LT.

Core Tip: The incidence and prevalence of liver disease is rising all over the world. Hepatitis B is difficult to eradicate and the benefit of directly acting antiviral therapy for hepatitis C is partially offset by increasing IV drug abuse. Non-alcoholic fatty liver disease pandemic is on and alcohol related liver disease is rising alarmingly, both having no drug cure. Due to better management of complications, patients of chronic liver disease are living longer spiking the number of hepatocellular carcinoma (HCC) and patients needing liver transplantation (LT). Better diagnostics and therapeutics are available for fibrosis, portal hypertension, HCC and post LT management which are discussed.

INTRODUCTION

Chronic liver disease (CLD) and cirrhosis pose substantial health burden worldwide. In the period 2007-2017, the age standardised prevalence increased 10.4% with 1.5 billion cases in 2017[ 1 ]. Of the four chief etiology, hepatitis B virus (HBV) and hepatitis C virus (HCV) burden still remains high [though decreased due to availability of vaccination for HBV and directly acting antiviral therapy (DAA) for HCV] although with the non-alcoholic fatty liver disease (NAFLD) pandemic and increasing global alcohol consumption, they are fast catching up. NAFLD is the leading cause in developed nations, it is also gradually becoming important in newly developed nations like India, China[ 2 , 3 ]. The age stan-dardised prevalence of HBV/HCV related CLD rose by 9%/10.2% in the last decade whereas for NAFLD it was 23.5%[ 1 ]. The high HBV and HCV burden is mostly due to poor diagnostic coverage and linkage to treatment and care of the susceptible population.

The HBV pool is chiefly contributed to by the Western Pacific and Subsaharan Africa region (mostly tribals) and some southeast Asian countries (China, Vietnam, Thailand, Laos) where the load remains high despite the success of HBV vaccination programme at birth. It is the leading cause of hepatocellular carcinoma (HCC) in these countries[ 4 ]. Among some developed and newly developing nations where prevalence is intermediate to low, its burden is contributed by the indigenous tribal population like India, Australia[ 5 , 6 ] maintained through intracaste marriages, close living, tribal customs, illiteracy and poor access to health care resources. With the present attrition rate (present burden of 296 million from 350 million 3 decades back and present annual mortality of 8 lakh and addition of 1.5 million cases in 2019[ 7 ]), it is still a long way for natural elimination of the pool. In future some redistribution is also likely due to population migration from high to low endemicity regions. World Health Organization (WHO)’s ambitious programme for eradication of HBV by 2030 therefore incorporate the best preventive measures i.e. , increase vaccination at birth, prevent vertical and horizontal transmission among toddlers by treating at risk mothers, and scale up screening, care and treatment services. Curative treatment is difficult and < 20% who receive the currently approved drugs [interferon, nucleos(t)ide analog (NA) or combination as sequential/add on/switch therapy] achieve loss of HBsAg (functional cure). Combination strategies are less cost effective than first line NA monotherapy although this may lead to more HBsAg loss in some subgroup of HBV patients[ 8 ]. Even with long term NA monotherapy (Tenofovir disoproxil for 5 years) half fail to achieve fibrosis regression[ 9 ] and there is high relapse rate in e negative patients (RETRACT B study showing relapse rate of 47.8% at 6 mo, 68.9% at 12 mo, 83.4% at 48 mo)[ 10 ]. The other problem is the risk of relapse in previously exposed person or inactive HBsAg carriers (who constitute a sizable majority of the present pool not requiring drug therapy) needing immunosuppression (IS) or cancer chemotherapy. Fortunately, highly active antivirals are capable of controlling the virus and reducing the burden of advanced liver disease from HBV. The chief impediments to HBV functional cure are intrahepatic viral reservoir cccDNA with integrated sequencing, high HBsAg levels, and defective host innate and adaptive immune responses. Newer strategies target these e.g. , targeting HBV life cycle without damaging hepatocyte by inhibiting ccc DNA replenishment pathways or degrading them by entry inhibitors like Bulevirtide [used for HBV/HDV coinfection including post liver transplantation (LT)], nucleic acid polymer assembly inhibitors (Lonafarnib), CRISPR/Cas9 protein base editors (DNA endonucleases), siRNAs, core protein modulators (Morphothiadin,Vebicorvir, Bersacapavir) and antisense oligonucleotide (Bepirovirsen)[ 2 ], immunomodulation to safely eliminate infected cells. Potential targets in innate immune response pathway include pathogen recognition receptors [Toll-like receptors 7/8, retinoic acid-inducible gene (RIG)-1-like receptors and nucleotide-binding oligomerization domain (NOD)-like receptors], natural killer cells and antigen presenting cells (dendritic cells and Kupffer cells) whereas in adaptive immune response pathway, it includes modulation of HBV-specific CD4+ and CD8+ T cell (especially the relative functional and numerical deficiency of CD8+ T-cells by PD-1 checkpoint inhibitors), regulatory T cell, HBV-specific T and B cell (autologous, engineered or by vaccine).

Gratifying results have been obtained with the introduction of affordable short term (3-6 mo) DAA therapy for HCV (with sustained viral response rates of > 95%, decreased fibrosis and HCC) with increasing treatment coverage in newly developed and developing nations which have decreased the HCV burden to 58 million as of 2019[ 7 ]. HCV still remains the leading cause of HCC in the developed world (Western countries, Japan) though alcohol related liver disease (ALD)/NAFLD are fast taking the lead due to treatment with DAA. But challenges still remain like limited drug availability, interaction with other drugs used to treat comorbidities (HIV, coronary artery disease and hyperlipidemia), inability to afford even the low drug cost by patients who pay from their own pocket and increasingly recognized metabolic dysfunctions associated with hepatitis C. Even in Denmark, 50% HCV patients are yet to attend specialist care especially IV drug users[ 11 ]. HIV coinfection is also a deterrent for good treatment outcome for both HBV and HCV. In future the HCV pool is likely to be maintained by intravenous drug users and the increasing population with drug/alcohol abuse and other psychiatric disorders, those needing repeated blood transfusion (for haematological disorders, hemodialysis) and reinfection in those who continue to have risk factors even after cure by DAA. WHO recommends increased access to treatment by onsite diagnosis by dried blood spot and initiating treatment at point of care and by trained non specialist doctors and nurses at harm reduction centres.

NAFLD is the most common liver disease worldwide affecting about a quarter population with regional differences[ 12 ]. It is fast becoming the leading cause of cirrhosis in developed nations. Genetic inheritance (25%-34%), ancestry (HispanicAmerican/Asian/Indian > European > African American), advancing age and male sex are non modifiable whereas obesity (especially central), diabetes mellitus, hyperlipidemia and insulin resistance are modifiable risk factors. There is currently no approved pharmacological therapy for NAFLD apart from those treating the risk factors. Weight loss through dietary alteration, physical exercises and bariatric surgery leads to improved liver histology but only small percentage of patients can achieve and maintain the degree of weight loss needed for sustaining the benefit and 50% fail to improve histology[ 13 ]. Ursodeoxycholic acid (UDCA)/obeticholic acid (OCA), Vitamin E have no proven benefit. Therefore it is the hottest area of newer drug research which modulate key metabolic, inflammatory, and fibrogenic pathway. Pan PPAR agonists (Lanifibranor), GLP 1 agonists (Semaglutide), CCR 5 inhibitors (Leronlimab), thyroid hormone receptor agonist (Resmetirom) and hepatic SCD1 inhibitor (Aramchol) are in phase 2 and 3 clinical trial (Table ​ (Table1). 1 ). Other antifibrotic and disease modifying agents as well as genetic factors are discussed below. But considering the multiple risk factors and complex pathophysiology, it is unlikely that a panacea will be discovered soon.

Interrim results of selective drug trials for non-alcoholic fatty liver disease and liver fibrosis

NASH: Non-alcoholic steatohepatitis; ALT: Alanine transaminase.

Approximately 2 billion people worldwide consume alcohol of whom 283 million suffer from AUD[ 14 ]. ALD is most prevalent in the western world and in some affluent Asian countries (South Korea, Japan) though there is increasing global trend especially in newly industrialised southeast Asian nations (China, India, Vietnam, Thailand) where it was low due to traditional“dry” culture. ALD has become the leading cause of CLD/cirrhosis in India[ 5 ]. The recent coronavirus disease 2019 (COVID-19) pandemic has significantly increased the incidence of ALD in young adults. DALYs per 1000 people due to ALD was highest in India (2356.4), followed by the United States (467.9), China (466.3), Nigeria (424.5) and Indonesia (365.1). For alcohol related liver cancer, DALYs were highest for China followed by Vietnam, Russia, Thailand, India[ 15 ]. Consumption depends on age, sex, religion, culture, health status and national income distribution. Globally it is a tussle between national income from alcohol retail vs health expenditure for AUD, the latter being dismal even in developed nations. Being a fully preventable disease, WHO’s “best buys” are the most cost effective ways for prevention, i.e. , increasing taxation on alcoholic beverages, enforcing bans or comprehensive restrictions on exposure to alcohol advertising and restricting physical availability of retailed alcohol. A recent global study shows no safe dose for alcohol[ 16 ]. Abstinence can reverse fatty liver and halt the progression of CLD. It is responsible for 50% of deaths due to CLD because no specific drug therapy is available apart from some short term benefit of steroids in pure acute hepatitis. Tumour necrosis factor alpha, growth hormone, pentoxyfylline and antioxidants at best show mixed results from highly variable to weak, efficacy depending on the stage of disease. The unclear molecular mechanism of disease deter identifying treatment target and disincetivize drug development. Naltrexone, disulfiram and acamprosate helps to decrease addiction. Obesity and cigarette smoking are known risk factors so weight control and quitting smoking are routinely encouraged. A poor overall nutritional status (protein calorie malnutrition, micronutrient deficiencies) often accompanies ALD and correlates positively with the development of serious complications hence a well-conceived nutrition support by oral, enteral, and parenteral routes is an essential part of standard care. Recent evidence also strongly implicates intestinal dysbiosis in ALD progression. These targets are being addressed by trials of probiotics, fecal microbiota transplantation, growth factors (granulocyte colony stimulating factor, bovine colostrum), antioxidants (ω5 and synthetic fatty acids, S-adenosyl methionine + choline, N-Acetyl cysteine, vitamin C), in addition to liver regenerative biologics and device assisted behavioural alteration[ 17 ]. The other hindrances are disease stratification for early identification when it is most reversible, monitoring abstinence (as recidivism is high) and identifying risky drinking behaviour like binges. Various biomarkers under study for this purpose include circulating small noncoding RNAs, long noncoding RNAs, selective cytokines profiles, phosphatidyl ethanol and urine ethyl glucuronide and ethyl sulphate[ 18 - 21 ].

AUTOIMMUNE LIVER DISEASE

Autoimmune hepatitis appears to be increasing in incidence as a part of the general increase in immune mediated and allergic diseases resulting from decreasing infectious disease with mounting antibiotics use globally. Some antibiotics like nitrofuranotin, minocycline and coamoxyclav can induce autoimmune hepatitis by themselves and some antibiotic associated drug induced liver disease (DILI) may resemble autoimmune hepatitis. The standard treatment of autoimmune hepatitis is with steroids with/without azathioprine. Mycophenolate mofetil is a second line drug. Substantial advances in treatment of autoimmune cholangiopathies has been achieved with PPAR α agonist bezafibrate, FXR agonist OCA and recombinant FGF 19 (which alter bile acid synthesis along with antifibrogenic effect[ 22 ], see below) and drugs inhibiting intestinal apical sodium-dependent bile acid transporter (linerixibat, maralixibat, odevixibat)[ 23 ]. in addition to bile acid resins and UDCA. Combinations of such enterohepatic with cholehepatic and/or anti-fibrotic drugs could result in synergistic/additive effects in decreasing the fibrosis along with the pruritus.

ADVANCES IN DIAGNOSIS

Non invasive biomarkers of CLD (patented ones like fibrotest, fibrometer, Hepascore, ELF model and non patented ones like FIB 4 index, APRI, BARD, NFS) and elastography (fibroscan, MRE, point SWE, 2D-SWE, 3D Velacur™) or their combination (MEFIB, MAST, FAST) have been investigated across the whole spectrum of NAFLD to delineate the stage as well as correlating genes with liver fat, enzymes and fibrosis[ 24 ]. Novel ones like computerised tomography (CT) scan with objective measures of liver nodularity and shunts, multiparametric magnetic resonance imaging (MRI) (iron corrected T1, cT1), extracellular vesicles, microbiome (stool microbial profiles), biomarker for extracellular matrix remodelling (TGF-β, MMP, TIMP)[ 25 - 27 ] are being investigated. Graph convolution networks (a deep learning technique) is being tested for quantitative assessment of fibrosis[ 28 ].

GENETIC FACTORS IN CLD

Genetic factors are important in progression of all forms of CLD (ALD, NAFLD, metabolic associated fatty liver disease, chronic hepatitis) including HCC with interplay of genes involved in glucose, lipid and iron metabolism, insulin signalling, oxidative stress, inflammatory pathways and fibrogenesis. Most reliable fatty liver genes include PNPLA3, TM6SF2, HSD17B13, GCKR and MBOAT7 (associated with increased liver fat, NASH, cirrhosis, HCC). The evidence for others like MARC1, GPAM, APOE, ALDH1B, PCKS7, SERPINA1, HNF1A etc are less robust. Rare variants like APOB and MTTP are associated with an increased risk of fat accumulation leading to HCC while protecting at the same time against dyslipidemia and cardiovascular risk[ 24 ]. Polygenetic risk score [with/without clinical risk markers] are being investigated to stratify disease risk e.g. , PNPLA3 and TM6SF2 can become a reason for HCC surveillance whilst giving protection from cardiovascular complications[ 29 ] It can also help in proper drug selection. Genetic therapies in CLD include gene silencing approaches (PNPLA3, HSD17B13), CRISPR/Cas9-based approaches[ 30 ] (which alter responsible genes) and modulating genes involved in liver regeneration.

ACUTE HEPATITIS

The etiology of acute liver failure (in those with normal liver) varies in different countries at different times. Most commonly these include viruses (hepatitis A, B and E), DILI (CAM, anti tuberculous drugs, paracetamol, anticonvulsants, antibiotics), toxins (herbs, alcohol) and autoimmune flares; else these may precipitate acute liver failure in those with CLD (acute-on-chronic liver failure, ACLF). Hepatitis E virus may be associated with fulminant course in pregnancy. In tropical areas, malaria, dengue, enteric fever, leptospirosis and scrub typhus may also cause acute hepatitis. Traditionally Wilson disease and autoimmune hepatitis has been considered to cause acute liver failure but in adults majority of such acute flares occur on background CLD. Most acute hepatitis of viral etiology recover by themselves and of drug/toxin on their discontinuation, some DILI may need corticosteroid (especially those resembling autoimmune hepatitis). Bacterial infections respond to antibiotics. But the course of ACLF depends on the stage of the background CLD and the precipitating cause, alcohol having the worst outcome[ 31 , 32 ]. Undefined number of acute hepatitis are occurring recently due to COVID-19 infection, recreational drugs and alcohol.

PORTAL HYPERTENSION AND LIVER FIBROSIS

With the increasing prevalence of CLD, portal hypertension and its complications are also increasing. Refractory ascites/hepatorenal syndrome/hydrothorax are now being better managed with terlipressin, noradrenaline, midodrine, octreotide and long-term albumin supplementation (with its newly discovered wider pleiotropic non-oncotic properties positively impacting decompensated CLD). Sodium-dependent glucose cotransporter 2 inhibitors and Alfa pump are under trial. Endohepatology (the application of endoscopic ultrasound in liver disease treatment)[ 33 ] has brought about dramatic improvement in the treatment of variceal bleed by better delineation of collaterals and guided treatment (coiling, balloon retrograde transvenous occlusion of collaterals, glue injection in gastric varix), directed liver biopsy, portal pressure gradient measurement and deployment of dedicated esophageal stents. Pre emptive transjugular intrahepatic portasystemic shunt has been used for uncontrollable ascites or variceal bleed.

Fibrosis in the liver is caused by activated HSC whose biology connects damage, regeneration and cancer. Severe hepatic fibrosis represses regeneration and accumulation of senescent HSCs creates a pro-inflammatory, pro-fibrotic environment. Fibrogenesis inhibitor drugs resolve inflammation, cause loss of activated myofibroblasts, and ECM degradation Those under investigation (Table ​ (Table1) 1 ) include [FXR agonist OCA/cilofexor, acetyl-coenzyme A carboxylase inhibitor Firsocostat, ASK-1 inhibitor Selonsertib, CCR 2/5 inhibitor Cenicriviroc, FGF 19 analog Aldafermin/21 analogue Pegbelfermin, PPAR α/δ agonist Elafibranor, PPARγ agonist pioglitazone, PPAR α/γ agonist Saroglitazar, galectin 3 inhibitor Belapectin, CB1 antagonist Rimonabant, ECM production inhibitors like TIMP and MMP, lysyl oxidase 2 inhibitor Simtuzumab, HSP 47 inhibitor Pirfenidone, pan caspase inhibitor Emricasan and anti inflammatory lipids derivatives of PUFA (lipoxins, resolvins, protectins, and maresins)][ 34 - 36 ]. Statins have been found to have beneficial effect in halting the progress of CLD[ 37 ].

The chief cause of HCC in the West is hepatitis C followed by NAFLD and alcohol whereas it is hepatitis B in Asia and Africa. Eighty percent occur in low and middle resource countries. Screening programme for HCC in cirrhotics is cost effective and better cancer surveillance can be achieved by the recently developed GALAD (incorporating AFP, AFP-L3, PIVKA, age and sex) screening tool[ 38 ] along with radiologic strategies (contrast enhanced ultrasonogram using Li-RADS, multiphasic CT/aMRI scan) every 6 mo in high risk patients. Ninety percent HCC occur on background CLD which pose additional health risk to HCC itself. Liver biopsy carries risk hence liquid biopsy using detection of circulating tumor cells specific to HCC, mutation or methylation of circulating tumor DNA, and transcriptomic profiling of extracellular vesicles are promising. The widely followed BCLC staging system have been upgraded (to be more inclusive for surgery with/without downgrading of tumor) and provide better platform for optimal use of different treatment modalities like ablation, resection, LT, stereotactic body radiation, locoregional and systemic therapy. LT has been extended outside Milan criteria to include more patients by various expanded selection criteria with reduced but excellent long term results[ 39 ]. Cancer in non cirrhotic liver is treated by LT with better understanding of transplant oncology. Newer drugs like multikinase inhibitors (Lenvatinib, Regorafinib, Cabozantinib, Ramucirumab), checkpoint inhibitors (atezolizumab, bevacizumab, durvalumab, pembrolizumab, nivolumab, ipilimumab and tremelimumab) are now available over sorafenib for systemic therapy with better outcome[ 40 ]. Limitations are their unclear safety profile in Child Pugh stage B, best response not more than 50%, unclear treatment sequence and use in early stage of tumor.

ALD is now the predominant cause for LT followed by HCV and NAFLD in the West[ 41 ] whereas it is still HBV/HCV in the East with ALD at its heels[ 42 ]. With good patient selection, the present 1 year survival is 90% and 5 year of 70%. Good outcome has been substantially influenced by betterment of surgical techniques, perioperative management, organ preservation (normothermic machine perfusion), recipient selection (through organ sharing network), post transplant immunosuppressive management and of viral etiology ( DAA for HCV. Bulevirtide/NA for HBV/HDV). The challenge of limited organ availability has been addressed by accepting marginal and extended criteria donors (donors of cardiac death, 30%-60% steatotic liver explant without inflammation, HBsAg and HCV positive donors), split liver grafts and live donor transplant especially in Asia[ 41 ]. Liver regeneration based approaches like stem cell therapy and organ bioengineering can also help. Most post transplant morbidity arise from prolonged use of immunosuppressive with resultant infections, hypertension, dyslipidemia, cardiovascular events, renal failure, malignancy and chronic organ rejection. Long term outcome can be improved by minimising/late introduction of standard IS or withdrawing it completely (20% become operationally tolerant) and using less toxic IS drugs (mToR inhibitors, interleukin 2 receptor blockers). With increased understanding of transplant immunology, research is on whether IS can be completely withdrawn after finite treatment by modulating recipient immunity (by CD4 Treg cells, regulatory dendritic cells or hematopoietic stem cell transplantation)[ 43 ]. The problem of HCV relapse leading to cirrhosis in 30% has been addressed by DAA. Present challenges for liver transplant are: (1) High alcohol recidivism; (2) ACLF grade 3 and severe acute alcohol related hepatitis; (3) NAFLD/non-alcoholic steatohepatitis with high comorbities; (4) Frailty in advance CLD; (5) Recipient and caregiver challenges; (6) Genetic variants; and (7) COVID-19. Severe acute respiratory syndrome coronavirus vaccine fails to decrease mortality as the patient's immunity is already weakened.

Artificial intelligence and digital transformation of various diagnostic modalities, decision making tool and management will further advance the treatment of liver diseases.

The current scenario of the epidemiology and the advances in diagnosis and treatment of liver diseases including their complications are summarised in this review.

Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Peer-review started: July 10, 2022

First decision: July 31, 2022

Article in press: September 21, 2022

Specialty type: Gastroenterology and hepatology

Country/Territory of origin: India

Peer-review report’s scientific quality classification

Grade A (Excellent): A

Grade B (Very good): B

Grade C (Good): 0

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Ji G, China; Rodrigues AT, Brazil S-Editor: Gao CC L-Editor: A P-Editor: Gao CC

Liver Disease Research

Cldf has a strong track record of supporting research, with over £9 million of research funding delivered since 1980. most of this money has been donated by families, friends, relatives and other supporters..

CLDF has a strong track record of supporting research, with over £9 million of research funding delivered since 1980. Most of this money has been donated by families, friends, relatives and other supporters. Research not only enhances our understanding of childhood liver disease and improves available treatments, but it gives children, young people and their families hope for a better future.

CLDF has funded a wide range of projects including clinical, lab based, and social science research, all focused on aspects of childhood liver disease.

Making an application for funding

The decision to award funding is made by CLDF’s Scientific Committee. They are a group of experts who ensure research funded by CLDF is rigorous and is most likely to have a positive impact on children with liver disease and their families. Due to the COVID-19 pandemic and the subsequent cost of living crisis, CLDF is facing unprecedented fundraising challenges and the board has taken the difficult decision not to accept any applications for new research grants during 2023. We shall continue to support all our existing projects which you can find out more about here.

CLDF remains committed to research and we will continue to monitor and review the situation and update this page when we able to reopen for new applications.

For any enquiries relating to Research, please email [email protected] .

Research Strategy

Priorities for paediatric liver research funding 2015 -2020.

The following are identified as priority areas for research funding:-

• Biobank and bio-markers
• National Paediatric Liver Diseases Register
• National Register of long term outcome for liver transplant
• Prevalence/epidemiology
• Different treatment modes
• Neonatal cholestasis
• Outcomes in adults of childhood liver diseases, including liver transplantation
• Clinical research including relevant drug trials in children and the long term effects of medication
• Quality of life
• Survivorship
• Transition to adult services
• Compliance and adherence

Underlying principles in research delivery

In funding and delivering its research programme, the following underlying principles will be paramount:-

• CLDF gives preference to multi-centre, collaborative research
• CLDF encourages research which focuses on added value
• CLDF is keen to identify how and where its research portfolio fits into the wider research programme and agenda
• CLDF recognises that given its funding available it is more likely to fund work in the early stages of research topic continuum, particularly when it is one which is capable of being taken from bench to bedside. It will monitor its programme for up to seven years beyond the end of a project
• CLDF expects dissemination of outputs and outcomes to both lay and professional audiences
• CLDF recognises the importance of the national and international research picture including the National Institute for Health Research (NIHR) and will take note of topical issues in applying its research priorities
• CLDF will expect its Principal Investigators to know of and apply to relevant networks and schemes beyond CLDF funded research
• Research teams are expected to think ‘outside the box’ and are expected to consider enabling European and other collaborations

CLDF Research Funding Mechanisms

CLDF will fund research using the following mechanisms:

• Project Grants encompassing:
• Up to 3 years funding
• Staff costs
• Consumables costs
• CLDF PhD student fellowships
• Small Grants Programme

CLDF Research Hub

CLDF will create projects during the strategy period, which increase opportunities for families and young people to learn more and be involved in research within the field. As part of this CLDF will aim to increase formal patient and public involvement in the design of research projects across the sector by creating a Research Hub, for researchers to obtain feedback from families and young people about the projects they are developing. By providing vehicles for such support CLDF could play a role in enabling a greater number of paediatric liver disease related research projects to be funded and undertaken.

Click here to view the Research Hub.

Strategy Review and Development process

This strategy has arisen out of a consultation over a period of nine months and incorporated consultation with the following stakeholders:

• Young People
• CLDF Scientific Committee
• Medical Professionals

Consultation took place via a variety of routes:

• Surveys for medical professionals circulated via BSPGHAN and through our own networks.
• Surveys for parents and young people circulated via social media platforms and direct invitation.
• A residential consultation weekend with families and young people.
• Feedback from our 2014 National Conference.
• One to One meetings with medical, psychology and nursing staff at Kings College Hospital, Birmingham Children’s Hospital and Leeds General Infirmary
• Discussion and ratification made at Scientific Committee and Trustee Board Meetings.

Use of animals and indirect costs

CLDF complies with the stance of the Association of Medical Research Charities (AMRC) on the use of animals in research as well as with indirect costs. Find out more about the use of animals in research, indirect costs and other policies on the  AMRC website .

Funding Research

Cldf offers funding to researchers through a number of different programmes:.

• £10,000 Grants Programme
• PhD Programme

Use of funds

• Applications will only be accepted for work based in the UK
• Applications must be in line with the CLDF Research Strategy
• The funds can be used widely but applicants must always be able to demonstrate how it can contribute to knowledge in paediatric hepatology
• Funds will not be awarded retrospectively

How we make decisions on the research we fund

CLDF’s Scientific Committee ensures the charity uses its funds to support the highest quality research to benefit the lives of children and young people affected by childhood liver disease now and in the future. The committee’s role is to assess applications for CLDF medical, social science and/or nursing award(s) and make recommendations for funding to the Board of Trustees. Here’s a link to the committee membership .

When we receive applications for funding the first thing we do is triage the applications, making sure they fit with the research strategy. Those that do move to the next stage….

Peer review takes place. This is where scientific experts look at an application and consider the scientific merit of the application and the likelihood that it will have an impact on the understanding, care or treatment of liver disease in children and young people. Each application is reviewed in detail by 2 or 3 reviewers. We make sure that the reviews are not biased and all reviewers have to sign up to our conflicts of interest policy and make us aware of any conflicts they may have .

A member of the Scientific committee will be assigned the role of ‘lead discussant,’ for each application this means that they are responsible for leading the discussion about the application and the peer reviews at the panel meeting, where the whole scientific committee considers the applications.

In preparation for the panel meeting all committee members receive the applications and peer reviews, for all applications that they do not have a conflict of interest for. A conflict of interest might be that the application comes from a colleague, or someone at the same institution. Detailed discussions and appraisal take place at the meeting. Individual committee members then rank the applications in order of their importance. Once collated the committee reviews the collective rankings. Further discussion can take place where there are not clear results. The final choice of successful applications is then made……

The trustees of the charity are then told about the scientific committees recommendations and funding decisions are made.

The applicants for the research round are informed of the outcome and receive feedback on their applications, for unsuccessful projects.

Successful projects are required to keep CLDF up to date on the progress of their research. At least once per year the scientific committee meets to review the formal progress reports from current grant holders and advise trustees whether to grant subsequent years’ funding and the general progress of the research portfolio.

Apply for a CLDF Research Grant

The decision to award funding is made by CLDF’s Scientific Committee. They are a group of experts who ensure research funded by CLDF is rigorous and is most likely to have a positive impact on children with liver disease and their families.

Due to the COVID-19 pandemic and the subsequent cost of living crisis, CLDF is facing unprecedented fundraising challenges and the board has taken the difficult decision not to accept any applications for new research grants during 2023. We shall continue to support all our existing projects.

For any enquiries relating to Research, please email  [email protected] .

Current Research Projects

Dr eirini (serena) kyrana, dr steffen hartleif, dr nicola ruth, dr ivana carey, dr girish gupte, dr emer fitzpatrick, dr gary reynolds, dr hassan rashidi, professor amin rostami and dr jill barber, dr luke boulter, professor alastair sutcliffe, dr claudio angione, dr vandana jain, professor deborah gill.

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• Open access
• Published: 20 April 2024

Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study

• Yue Xiao 1 ,
• Yanfei Chen 1 ,
• Ruijian Huang 1 ,
• Feng Jiang 1 ,
• Jifang Zhou 1   na1 &
• Tianchi Yang 2   na1  

BMC Medical Research Methodology volume  24 , Article number:  92 ( 2024 ) Cite this article

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The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment.

A dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve. The Shapley Additive exPlanations (SHAP) method was used to interpret the variable contributions of the superior model.

A total of 7,071 TB patients were identified from the regional healthcare dataset. The study cohort consisted of individuals with a median age of 47 years, 68.0% of whom were male, and 16.3% developed DILI. We utilized part of the high dimensional propensity score (HDPS) method to identify relevant variables and obtained a total of 424 variables. From these, 37 variables were selected for inclusion in a logistic model using LASSO. The dataset was then split into training and validation sets according to a 7:3 ratio. In the validation dataset, the XGBoost model displayed improved overall performance, with an AUROC of 0.89, an AUPR of 0.75, an F1 score of 0.57, and a Brier score of 0.07. Both SHAP analysis and XGBoost model highlighted the contribution of baseline liver-related ailments such as DILI, drug-induced hepatitis (DIH), and fatty liver disease (FLD). Age, alanine transaminase (ALT), and total bilirubin (Tbil) were also linked to DILI status.

XGBoost demonstrates improved predictive performance compared to RF and LASSO logistic in this study. Moreover, the introduction of the SHAP method enhances the clinical understanding and potential application of the model. For further research, external validation and more detailed feature integration are necessary.

Peer Review reports

Drug-induced liver injury (DILI) presents significant challenges in the context of tuberculosis (TB) treatment. Anti-TB drugs exhibit noteworthy involvement in the occurrence of DILI [ 1 , 2 ], and the lack of certain early-detection biomarkers [ 3 ] further poses challenges to the timely diagnosis and management of DILI. This absence of early detection may result in treatment interruptions and failures amongst TB patients [ 4 , 5 ], impeding global TB eradication efforts [ 6 ]. In China, the elevated incidence rates of DILI in comparison to western nations highlight the potential involvement of traditional Chinese medicines (TCM) and herbal medicines in the development of DILI [ 7 ]. This requires addressing various challenges and complexities associated with DILI assessment in a comprehensive and objective manner. Therefore, the primary objective of this study is to develop an optimal predictive model for assessing DILI status, with a specific focus on TB patients within the Chinese context.

The emergence of machine learning (ML) algorithms presents an exciting opportunity to enhance DILI prediction models [ 8 ]. Among these, eXtreme Gradient Boosting (XGBoost) [ 9 ] and random forest (RF) [ 10 ] stand out as two widely-used ensemble learning techniques, each distinguished by its algorithmic approach and features. Selecting the most suitable option between them hinges on the particular characteristics of the data and the prediction objective. Therefore, it is often advisable to conduct experiments with both models to compare their performance.

Nevertheless, one of the primary challenges in implementing ML algorithms in clinical settings is interpreting the outcomes of the models [ 11 , 12 ]. The Shapley Additive exPlanations (SHAP) framework [ 13 ] provides insights into the influence of various features on model predictions and the effect of these features on the DILI status in individuals, thus bridging the interpretability gap.

This study focuses on the development and validation of a prediction model for DILI in the context of TB treatment by using advanced ML algorithms with SHAP interpretability. Through this endeavor, we aim to achieve a balance between accurate prediction and the interpretability of the model, which is crucial for its clinical application.

Data source

The study participants comprised individuals diagnosed with TB at specified hospitals in Ningbo from 1st January 2015 to 2nd January 2020, initially referred by the Chinese Center for Disease Control and Prevention (CDC) [ 14 ]. Thereafter, they were connected to administrative records obtained from the electronic health records (EHR) system employed by the local government [ 15 ]. The merged dataset comprised demographic information, hospitalization records (both inpatient and outpatient), laboratory tests, and medication profiles.

Exclusion criteria

To ensure consistency in the identification of covariates, individuals with only one health care encounter during the study period were excluded. Furthermore, individuals without ethnicity information and those under 18 years old at diagnosis were not included in the study. The exclusion criteria also filtered out misdiagnosed cases of DILI and liver injuries attributed to known factors like alcohol-related liver disease, non-alcoholic fatty liver disease (NAFLD), and viral hepatitis unrelated to drug-induced causes. The detailed flowchart is presented in Fig.  1 .

Study schema for subject selection. Abbreviations: EHR, Electronic healthcare record; CDC, Center for Disease Control and Prevention

Baseline laboratory result collection

For patients included in the study, we defined the baseline period for collecting laboratory test results as from January 1, 2015, to the day before the index diagnosis of pulmonary tuberculosis, as shown in Supplemental Fig.  1 . Additionally, liver function test indicators such as alanine transaminase (ALT) or alkaline phosphatase (ALP) were simultaneously examined.

To address the issue of varied baseline definitions in laboratory testing, we utilized two main strategies. Firstly, we employed a binary variable approach to categorize laboratory testing indicators as abnormal or normal, by comparing their values with predefined normal ranges. Secondly, we utilized ratio-based representation to quantify indicator abnormalities, such as calculating ALT multiples relative to the upper limit of the normal (ULN) range.

Factor identification

In our research, we followed the initial steps outlined in the high dimensional propensity score (HDPS) methodology by Schneeweiss et al. [ 16 ]. First, we identified 24 common factors, such as age and gender, to integrate into our models. We then categorized our data into four dimensions: outpatient records, inpatient records, laboratory test records, and medication records. Following the approach of Chen et al. [ 17 ], we identified the top 500 most prevalent codes within each dimension. Next, we evaluated code recurrence, classifying codes into three binary variables based on their frequency of occurrence over a 12-month baseline period. This yielded a total of 4*500*3 binary factors. Using a multiplicative model considering binary factor and DILI status, we prioritized covariates and selected the top 400 for inclusion in our final model based on an arbitrary cutoff recommendation [ 18 , 19 ]. Finally, considering the previously specified 24 variables, our model training ultimately involved incorporating a total of 424 factors.

DILI diagnostic process

The determination of DILI outcomes followed the revised criteria set forth by the Chinese Society of Hepatology (CSH) DILI consensus, as outlined in Supplemental Table  1 [ 20 ].

Extraction of features used in prediction model

The LASSO regression method, aimed at reducing the number of variables and preventing overfitting [ 21 ], was applied to extract significant features for constructing the logistic model. Additionally, both the XGBoost and RF algorithms come equipped with their own feature selection techniques tailored to enhance their respective models.

Statistical analysis

The study reported the features of both the non-DILI and DILI groups by mean and standard deviation (SD) or as numbers and percentages whenever necessary. Laboratory variables were represented in median and quartiles [ 22 ]. The Kruskal–Wallis rank sum test was used for continuous variables, while the chi-square test was used for categorical variables. These analyses were conducted using the statistical software packages SAS 9.4 and R 4.0.3. A statistically significant result was determined with a two-sided P -value below 0.05.

Data splitting

In order to create training and validation sets, a stratified random function in R randomly assigned records at a 7:3 ratio, following conventional practices.

Parameter optimization

To optimize the parameters of the XGBoost and RF models, a ten-fold cross-validation process combined with grid search [ 23 ] was employed. This approach entailed identifying the hyperparameter set that yielded the maximum receiver operating characteristic (ROC). A detailed breakdown of the grid search particulars and optimal results can be found in Supplemental Table 2 .

Model evaluation and interpretation

To assess the model's capacity to differentiate between positive and negative cases, we computed both the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) [ 24 ]. Calibration was examined through reliability diagrams and Brier scores. Furthermore, the model's clinical utility was evaluated using decision curve analysis. The SHAP technique was utilized to delve deeper into variable contributions. A comprehensive overview of the workflow can be found in Supplemental Fig. 2 .

Participant and factor identification

The preliminary linkage of data yielded 12,087 instances. Following the application of exclusion criteria, a total of 7,071 subjects were identified as suitable for inclusion in the study.

During a one-year baseline period, we identified the 500 most prevalent codes across each data dimension (outpatient, inpatient, medication, and laboratory test) using the International Classification of Diseases-Tenth Revision (ICD-10), Current Procedural Terminology (CPT), and generic drug names. These items were then categorized into three binary variables: "ever occurring", "sporadically occurring", and "frequently occurring", indicating their recurrence. This process resulted in a total of 6,000 variables, from which the top 400 binary empirical variables were chosen based on their highest risk ratios associated with DILI status. Additionally, the final model incorporated 24 predefined baseline variables, such as gender, age, education level, medication, and maximum ratio of ULN for ALT, ALP, and Tbil, etc. Out of an initial pool of 424 features, 37 were selected for logistic model development using LASSO. The factors included in the LASSO logistic model are detailed in Supplemental Table 3 .

Epidemiology of DILI

The incidence of DILI was observed to be 16.3% overall, with a slightly higher observed in female patients (17.3% vs. 15.8%, p  = 0.134). Detailed demographics and clinical information are outlined in Table  1 . Compared to non-DILI individuals, those with DILI demonstrated lower educational attainment and a higher incidence of abnormal baseline levels in ALT and ALP [ALT: 91 (7.9%) vs. 273 (4.6%), p  < 0.001; ALP: 100 (8.7%) vs. 400 (6.8%), p  = 0.023]. Individuals of middle age, females, and those with pre-existing chronic liver conditions were found to have a higher susceptibility to DILI. Significant associations with DILI were identified for certain drugs, including pyrazinamide (PZA), isoniazid (INH), traditional Chinese medicines (TCM), and hepatoprotective agents such as silymarin and glycyrrhetinic acid.

Model development and validation

The XGBoost and RF models were constructed using optimal parameters obtained through the previously mentioned GridSearchCV method. The LASSO logistic model was constructed with the aforementioned variables. Internal validation was conducted by partitioning validation sets, resulting in a comparison of model performance among the three models showcased in Table 2 . The XGBoost model exhibited slightly superior discriminatory ability when compared with the RF and LASSO logistic model, with AUROC values of 0.89 versus 0.88/0.85 and AUPR values of 0.75 versus 0.73/0.67, respectively, as shown in Figs. 2 and 3 . The RF model demonstrated increased recall with a score of 0.78, while the XGBoost model achieved the highest F1-score of 0.57. Calibration was evaluated through ten predictive probability-based bins and verified by the reliability diagram presented in Fig. 4 , supported by a Brier score of 0.08, indicating the impressive alignment in calibration between the XGBoost and LASSO logistic models. Extensive analysis of the decision curve revealed positive net benefits for all models. Notably, XGBoost models outperformed both the RF and LASSO logistic models within the threshold range of approximately 0.2 to 0.5, as demonstrated in Fig.  5 .

Comparison of the AUROC of the XGBoost, logistic and random forest in the validation set

Comparison of the AUPR of the XGBoost, logistic and random forest in the validation set

Comparison of the calibration curve of the XGBoost, logistic and random forest in the validation set

The decision curve of the XGBoost, logistic and random forest in the validation set

Model interpretation

Revealing the factors that influenced the outperformed model's predictions, Fig.  6 laid out the most paramount features of XGBoost (with feature importance > 0.01). Of note, historical occurrences of DILI, DIH, and fatty liver disease (FLD) during the baseline phase were consistently highlighted. Moreover, the ULN for ALT, ALP and Tbil were also identified as critical factors. The SHAP values calculated for the XGBoost model, as shown in Supplemental Fig. 3 , indicate that individuals who had chronic liver disease during baseline were more likely to be in DILI status. Interestingly, we found that those with a lower educational level were more susceptible to DILI status. To gain a deeper understanding of the underlying mechanism and the effects of features in the XGBoost model, we randomly selected two typical patients from the dataset. Furthermore, we created force plots to visualize their decision process, as illustrated in Supplemental Fig.  4 and Supplemental Fig.  5 . The average SHAP value was 0.168, where yellow indicates a positive impact and purple represents a negative impact. In Supplemental Fig.  4 , the identified patient with a SHAP value of 1.06, surpassing the average, is likely to develop DILI. The significant influencing factor is being diagnosed with DILI or DIH at least once during the baseline period. The same rationale applies to the identified patient as depicted in Supplemental Fig.  5 . Additionally, Supplemental Fig.  6 presents a force plot that captures the aggregate effect in the validation set.

Top important features selected by XGBoost (> 0.01). Abbreviations: ODILIO, outpatient drug-induced liver injury, once occurring; ODIHO, outpatient drug induced hepatitis, once occurring; ODIHS, outpatient drug induced hepatitis, sporadically occurring; IDIHO, inpatient drug induced hepatitis, once occurring; ODILIS, outpatient drug induced liver injury, sporadically occurring; IDIHF, inpatient drug induced hepatitis, frequently occurring; IDILIO, inpatient drug induced liver injury, once occurring; ODILIF, outpatient drug induced liver injury, frequently occurring; TBIL, total bilirubin; ALP, alkaline phosphatase; IDILIS, inpatient drug induced liver injury, sporadically occurring; ALT, alanine aminotransferase; FLD, fatty liver disease

To our knowledge, this study represents the initial attempt to evaluate the prediction for DILI in an Asian population, predominantly of Han ethnicity, with TB using regional electronic health records. We observed slightly enhanced discrimination abilities in ML models compared to the logistic model. While logistic regression offers better clinical generalizability, it struggles with overfitting and handling missing variables, resulting in overall weaker performance than anticipated. In contrast, both XGBoost and RF employ more advanced techniques. XGBoost utilizes gradient boosting, progressively building weak learners and effectively capturing non-linear relationships with built-in regularization. On the other hand, RF, a bagging ensemble method, constructs independent decision trees on random subsets of data, resulting in robust averaging but with less explicit regularization. XGBoost excels in capturing intricate non-linear patterns, making it suitable for tasks involving complex and dynamic interactions like predicting DILI during TB treatment. Its training efficiency is also evident when handling large datasets. RF, with its robust averaging, is well-suited for further application in diverse datasets but may encounter challenges in effectively capturing subtle non-linear patterns among multiple explanatory variables.

Several prior studies have identified risk factors associated with DILI during TB treatment, involving chronic liver disease, specific drug combinations, age, and various demographic characteristics [ 25 , 26 , 27 ]. Lammert et al. [ 28 ] suggested an increased risk of DILI in patients with chronic liver disease indicative of NAFLD. Chang et al. [ 29 ] indicated a significant rise in hepatotoxicity risk associated with adding PZA to INH and RIF. Hosford et al. [ 30 ] established a notable elevation in hepatotoxicity risk among individuals over 60 years of age through a systematic literature review. Abbara et al. [ 2 ] found low patient weight, HIV-1 co-infection, higher baseline ALP levels, and alcohol intake were risk factors. Thus, in our model, we predefined enzyme levels, utilization of anti-TB drugs such as PZA, INH, and RIF, hepatoprotective agents such as silymarin and glycyrrhetinic acid, alcohol intake, and demographic variables such as age, gender, education level, ethnicity, profession as predictors. In the ultimate XGBoost model, the contribution weights for chronic liver disease, ULN of ALT, ALP, Tbil, and age surpass 0.01, consistent with earlier research discoveries.

Currently, a range of predictive models for DILI primarily operates at the molecular level in preclinical settings [ 31 ], utilizing diverse artificial intelligence assisted algorithms [ 32 ]. Minerali et al. [ 33 ] employed the Bayesian ML method, resulting in an AUROC of 0.81, 74% sensitivity, 76% specificity, and 75% accuracy. Xu et al. [ 34 ] proposed a deep learning model, achieving 87% accuracy, 83% sensitivity, 93% specificity, and an AUROC of 0.96. Dominic et al.'s Bayesian prediction model [ 35 ] demonstrated balanced performance with 86% accuracy, 87% sensitivity, 85% specificity, 92% positive predictive value, and 78% negative predictive value. In the clinical stage, only Zhong et al. introduced a single tree XGBoost model with 90% precision, 74% recall, and 76% classification accuracy for DILI prediction, using a clinical sample of 743 TB cases [ 36 ]. In our study, we leveraged regional healthcare data and employed the XGBoost algorithm. The model exhibited 76% recall, 82% specificity, and 81% accuracy in predicting DILI status. Our approach was proven robust, as evidenced by a mean AUROC of 0.89 and AUPR of 0.75 upon tenfold cross validation. During the clinical treatment stage, our model exhibited high levels of accuracy and interpretability.

The choice of a cutoff in a DILI prediction model is crucial and depends on specific study goals and requirements. Various studies have investigated optimal cutoff values in DILI prediction models to enhance understanding and prediction accuracy. For instance, in a study focused on drug-induced liver tumors, the maximum Youden index was utilized to determine the ideal cutoff point [ 37 ]. Another study, aimed at predicting DILI and cardiotoxicity, determined 0.4 as the optimal cutoff value using chemical structure and in vitro assay data [ 38 ]. Similarly, a system named DILIps, designed to predict DILI in drug safety, utilized the ROC curve to select the best cutoff value [ 39 ]. Given the imbalanced dataset in our study, we found the precision recall curve method seemed to be more appropriate. Additionally, considering the severe consequences of DILI, prioritizing the detection of DILI suggests choosing a lower cutoff to maximize sensitivity. Thus, in our study, we opted for the maximum Youden index as the best cutoff.

However, the acceptability of ML in the medical community faces a significant hurdle regarding interpretability, particularly in settings where clinical decisions are paramount. Our research employed SHAP strategies to illuminate the complex mechanisms of the XGBoost model.

Strengths and limitations

The study utilized a large dataset of over 7,000 TB patients to develop a robust model and comprehensively included clinical, demographic, and biochemical variables to improve predictive accuracy. Furthermore, the model incorporates SHAP analysis to improve interpretability. However, as we embark on the integration of ML into clinical settings, a vital concern persists regarding the generalizability of models [ 40 ]. While our model demonstrates enhanced predictive accuracy, it's important to recognize the inherent limitations stemming from the lack of external validation. Patient characteristics [ 41 ] and drug interactions [ 42 ] may differ widely across populations. This underscores the importance of validating models on diverse patient cohorts and geographical regions. Moreover, the study's reliance on a data-driven approach and the inherent complexity of integrating ML models into clinical practice present additional limitations [ 43 ]. Additionally, the dependence on clinical diagnosis for DILI and the potential influence of unmeasured variables on model accuracy are acknowledged. While the study's findings offer valuable insights, careful consideration is warranted when interpreting them.

Conclusions

XGBoost shows improved predictive performance compared to RF and LASSO logistics in this study. Moreover, introducing the SHAP method enhances the clinical understanding and potential application of the model. For further research, external validation and more detailed feature integration are necessary.

Code availability statement

To enhance reproducibility and facilitate peer review, we uploaded the code used for model fitting. The source code associated with this research is available on the GitHub repository ( https://github.com/cpu-pharmacoepi/TB-DILI ). For inquiries or assistance related to the code, please contact 1,020,202,[email protected].

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Data cannot be shared publicly because of privacy and confidentially of the TB patients in Ningbo, Zhejiang, China.

Abbreviations

Alkaline phosphatase

Alanine transaminase

Area under the precision recall curve

Area under the receiver operating characteristic curve

Center for Disease Control and Prevention

Current procedural terminology

Chinese Society of Hepatology

Drug-induced hepatitis

• Drug-induced liver injury

Electronic healthcare record

Fatty liver disease

High dimensional propensity score

International Classification of Diseases-Tenth Revision

International normalized ratio

Least Absolute Shrinkage and Selection Operator

• Machine learning

Nonalcoholic fatty liver disease

Pyrazinamide

Random Forest

Receiver operating characteristic

Standard deviation

Shapley Additive exPlanations

Standardized mean difference

• Tuberculosis

Total serum bilirubin

Traditional Chinese medicine

Upper limit of normal

EXtreme Gradient Boosting

Jiang F, Yan H, Liang L, et al. Incidence and risk factors of anti-tuberculosis drug induced liver injury (DILI): Large cohort study involving 4,652 Chinese adult tuberculosis patients. Liver Int. 2021;41(7):1565–75.

Article   CAS   PubMed   Google Scholar  

Abbara A, Chitty S, Roe JK, et al. Drug-induced liver injury from antituberculosis treatment: a retrospective study from a large TB center in the UK. BMC Infect Dis. 2017;17:231.

Article   PubMed   PubMed Central   Google Scholar  

Council for International Organizations Medical Sciences. Drug-induced liver injury. Geneva: CIMOS; 2020. Available from: https://cioms.ch/wp-content/uploads/2020/06/CIOMS_DILI_Web_16Jun2020.pdf . Accessed 01 Mar 2021

Nahid P, Dorman SE, Alipanah N, et al. Official American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America Clinical Practice Guidelines: Treatment of Drug-Susceptible Tuberculosis. Clin Infect Dis. 2016;63(7):e147–95.

Stravitz RT. WM Lee. Acute liver failure The Lancet. 2019;394(10201):869–81.

CAS   Google Scholar  

World Health Organization. Global tuberculosis report. Geneva: WHO; 2020. Available from: https://www.who.int/tb/publications/global_report/en/ .

Shen T, Liu Y, Shang J, et al. Incidence and Etiology of Drug-Induced Liver Injury in Mainland China. Gastroenterology. 2019;156(8):2230-2241.e11.

Article   PubMed   Google Scholar  

Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. 2021;2:160.

Article   Google Scholar  

Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM; 2016;785–795.

Breiman L. Random Forests. Mach Learn. 2001;45:5–32.

Bjerregaard SS. Exploring predictors of welfare dependency 1, 3, and 5 years after mental health-related absence in Danish municipalities between 2010 and 2012 using flexible machine learning modelling. BMC Public Health. 2023;23(1):224.

Alan I, Andrew P, Catherine BH. Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. J Comput Graph Stat. 2022;31(3):766–78.

Lu S, Chen R, Wei W, et al. Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions. AMIA Annu Symp Proc. 2022;2021:813–22.

PubMed   PubMed Central   Google Scholar  

Jiang WX, Huang F, Tang SL, et al. Implementing a new tuberculosis surveillance system in Zhejiang, Jilin and Ningxia: improvements, challenges and implications for China’s National Health Information System. Infect Dis Poverty. 2021;10(1):22.

Liu Z, Zhang L, Yang Y, et al. Active Surveillance of Adverse Events Following Human Papillomavirus Vaccination: Feasibility Pilot Study Based on the Regional Health Care Information Platform in the City of Ningbo, China. J Med Internet Res. 2020;22(6): e17446.

Schneeweiss S. Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects. Clin Epidemiol. 2018;10:771–88.

Chen Q, Hu A, Ma A, et al. Effectiveness of Prophylactic Use of Hepatoprotectants for Tuberculosis Drug-Induced Liver Injury: A Population-Based Cohort Analysis Involving 6,743 Chinese Patients. Front Pharmacol. 2022;20(13): 813682.

Polinski JM, Schneeweiss S, Glynn RJ, et al. Confronting “confounding by health system use” in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 2):90–8.

Schneeweiss S, Rassen JA, Glynn RJ, et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512–22.

Yu YC, Mao YM, Chen CW, et al. CSH guidelines for the diagnosis and treatment of drug-induced liver injury. Hepatol Int. 2017;11(3):221–41.

Sun L, Wang Q, Liu M, et al. Albumin binding function is a novel biomarker for early liver damage and disease progression in non-alcoholic fatty liver disease. Endocrine. 2020;69:294–302.

James G, Witten D, Hastie T, et al. An introduction to statistical learning: with applications in R. New York: Springer; 2013.

Book   Google Scholar  

Sattar N, Scherbakova O, Ford I, et al. Elevated alanine aminotransferase predicts new-onset type 2 diabetes independently of classical risk factors, metabolic syndrome, and C-reactive protein in the west of Scotland coronary prevention study. Diabetes. 2004;53(11):2855–60.

Coyner AS, Chen JS, Singh P, et al. Single-Examination Risk Prediction of Severe Retinopathy of Prematurity. Pediatrics. 2021;148(6): e2021051772.

Cao J, Mi Y, Shi C, et al. First-line anti-tuberculosis drugs induce hepatotoxicity: A novel mechanism based on a urinary metabolomics platform. Biochem Biophys Res Commun. 2018;497(2):485–91.

Tweed CD, Wills GH, Crook AM, et al. Liver toxicity associated with tuberculosis chemotherapy in the REMoxTB study. BMC Med. 2018;16(1):46.

Patterson B, Abbara A, Collin S, et al. Predicting drug-induced liver injury from anti-tuberculous medications by early monitoring of liver tests. J Infect. 2021;82(2):240–4.

Lammert C, Imler T, Teal E, et al. Patients With Chronic Liver Disease Suggestive of Nonalcoholic Fatty Liver Disease May Be at Higher Risk for Drug-Induced Liver Injury. Clin Gastroenterol Hepatol. 2019;17(13):2814–5.

Chang KC, Leung CC, Yew WW, et al. Hepatotoxicity of pyrazinamide: cohort and case-control analyses. Am J Respir Crit Care Med. 2008;177(12):1391–6.

Hosford JD, von Fricken ME, Lauzardo M, et al. Hepatotoxicity from antituberculous therapy in the elderly: a systematic review. Tuberculosis (Edinb). 2015;95(2):112–22.

Chen M, Bisgin H, Tong L, et al. Toward predictive models for drug-induced liver injury in humans: are we there yet? Biomark Med. 2014;8(2):201–13.

Vall A, Sabnis Y, Shi J, et al. The Promise of AI for DILI Prediction. Front Artif Intell. 2021;14(4): 638410.

Minerali E, Foil DH, Zorn KM, et al. Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI). Mol Pharm. 2020;17(7):2628–37.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Xu Y, Dai Z, Chen F, et al. Deep Learning for Drug-Induced Liver Injury. J Chem Inf Model. 2015;55(10):2085–93.

Williams DP, Lazic SE, Foster AJ, et al. Predicting Drug-Induced Liver Injury with Bayesian Machine Learning. Chem Res Toxicol. 2020;33(1):239–48.

Zhong T, Zhuang Z, Dong X, et al. Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study. JMIR Med Inform. 2021;9(7): e29226.

Linden A. Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. J Eval Clin Pract. 2006;12(2):132–9.

Ye L, Ngan DK, Xu T, et al. Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data. Toxicol Appl Pharmacol. 2022;1(454): 116250.

Liu Z, Shi Q, Ding D, et al. Translating clinical findings into knowledge in drug safety evaluation–drug induced liver injury prediction system (DILIps). PLoS Comput Biol. 2011;7(12): e1002310.

Fisher S, Rosella LC. Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health. 2022;22:2146.

Obermeyer Z, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–53.

Juurlink David N. Drug-drug interactions among elderly patients hospitalized for drug toxicity. JAMA. 2003;289(13):1652–8.

Luo W, Phung D, Tran T, et al. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res. 2016;18(12): e323.

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Acknowledgements

The authors thank all staff of the tuberculosis control centers, designated hospitals, community health service centers, and township health centers in ten counties/districts from Ningbo for their hard work and help in collecting clinical data. We also thank our colleagues from Ningbo Health Information Center for providing clinically relevant data for this study.

Disclosure of AI tools

We hereby disclose that generative AI tools were not utilized in the preparation or analysis of data presented in this manuscript. All methodologies and analyses were conducted utilizing established statistical and machine learning techniques as outlined in the Method section.

This research was supported by Zhejiang Medical Research Project(2018KY733) and Natural Science Foundation of Ningbo (2019A610386, 2019A610385). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

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Jifang Zhou and Tianchi Yang are both authors contributed equally to this work and shared corresponding authorship.

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School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China

Yue Xiao, Yanfei Chen, Ruijian Huang, Feng Jiang & Jifang Zhou

Institute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, No.237, Yongfeng Road, Ningbo, Zhejiang, China

Tianchi Yang

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All authors were involved in the design of the study, FJ and RH cleaned data and constructed the cohort; YC was involved in conceptualizing the study; YX and JZ were responsible for the analysis of the data and interpretation of the results.; YX, JZ and TY contributed to the drafting of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jifang Zhou or Tianchi Yang .

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Xiao, Y., Chen, Y., Huang, R. et al. Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study. BMC Med Res Methodol 24 , 92 (2024). https://doi.org/10.1186/s12874-024-02214-5

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Researchers earn $2.3 million grant to study generational cycle of maternal obesity, liver disease

Project includes testing an antioxidant for improving metabolic health

University of Oklahoma

Researchers from the University of Oklahoma College of Medicine recently earned a $2.3 million grant to study the generational cycle of maternal obesity and liver disease.

Credit: University of Oklahoma

Research increasingly suggests that when a woman with obesity becomes pregnant, a process of “fetal reprogramming” increases the risk that her baby will face problems like obesity, Type 2 diabetes and liver disease earlier in life.

To better understand how that reprogramming occurs, University of Oklahoma researchers recently earned a $2.3 million grant from the National Institutes of Health. They also will study whether an antioxidant called PQQ given to the mother can lower the risk of future metabolic problems for her offspring.

“Today in the United States, more than 40% of women of childbearing age are overweight or obese,” said OU College of Medicine researcher Karen Jonscher, Ph.D., who is leading the work of the grant with Dean Myers, Ph.D. “Research has shown that people whose mothers were obese during pregnancy have a higher risk for developing metabolic dysfunction-associated steatotic liver disease, a fatty liver disease that becomes progressively worse and can result in the need for a transplant. However, in offspring, it happens earlier in life and with more severe problems. The whole process seems to be accelerated in children who are born to mothers with obesity.”

Much of America’s obesity problem is attributed to eating a “Western-style” diet that is heavy on fats. However, even if a woman with obesity eats healthier during pregnancy, her offspring still face a higher risk of disease. Jonscher and Myers believe the key may be what is happening in the placenta — the interface between mother and fetus.

Obesity is essentially a low-grade, chronic inflammatory disease. Fat cells cause inflammation, which means the body’s white blood cells are in a constant state of activation and can damage other cells and tissues. Cholesterol and triglyceride levels rise, and blood pressure increases. Jonscher hypothesizes that the inflammation in pregnant women with obesity prompts the placenta to send a signal to the fetus’s stem cells, telling them to reprogram themselves to become more susceptible to the inflammation’s harmful effects.

“There is even some evidence that inflammation changes how nutrients are transported to the fetus so that fat is preferentially transported rather than the building blocks of proteins,” said Jonscher, an associate professor of biochemistry and physiology.

With the grant, Jonscher and Myers will try to prove that hypothesis. In addition, they will test an antioxidant called pyrroloquinoline quinone, or PQQ, for its ability to block or reverse fetal reprogramming. PQQ, found in fruits and vegetables, has anti-inflammatory properties, but if a person doesn’t eat a healthy diet, they are less likely to have adequate levels of PQQ.

In their preliminary studies in a preclinical research model, the researchers found that when PQQ is given to obese mothers, their offspring are protected from fatty liver disease in adulthood. Because women are generally advised not to take weight loss drugs during pregnancy due to potential harm to the fetus, the researchers hope PQQ is both safe and effective.

“Based on the data we have gathered so far, we believe that PQQ will create a healthier pregnancy,” Myers said. “The mother may still have a high body mass index, but PQQ appears able to lower inflammation and improve cholesterol and lipid levels. If we can improve the mother's health, we are also improving the function of the placenta, which will protect the fetus in a positive way. And if we can protect the placenta, nutrient transport will be improved with more amino acids and protein building blocks reaching the fetus instead of fats, as well as better oxygen flow.”

Myers, who is a professor in the Department of Obstetrics and Gynecology, often talks with his clinical colleagues who are caring for women with obesity during their pregnancies. Exercising and eating a healthy diet can be difficult for all people, pregnant or not, and physicians need another tool to help women become more metabolically healthy while pregnant.

“Our goal is to create a less-inflamed, healthier placenta,” he said. “Hopefully, PQQ will help the mother, too, because women with obesity who are pregnant have an increased risk for gestational diabetes. If our research with this grant is successful, we hope to move PQQ into clinical trials in a few years.”

About the project

Research reported in this news release is supported by the National Institute of Diabetes and Digestive and Kidney Diseases, a component of the National Institutes of Health, under award number 1R01DK139443-01. Additional support is provided by OU Health Harold Hamm Diabetes Center and Presbyterian Health Foundation. Myers holds the John W. Records Chair in Obstetrics and Gynecology.

About the University of Oklahoma

Founded in 1890, the University of Oklahoma is a public research university located in Norman, Oklahoma. As the state’s flagship university, OU serves the educational, cultural, economic and health care needs of the state, region and nation. OU was named the state’s highest-ranking university in U.S. News & World Report’s most recent Best Colleges list . For more information about the university, visit www.ou.edu .

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Original Source

Double Trouble for Alcohol-Associated Liver Disease

IRP Researchers Identify Two Types of Liver Damage and a Possible Treatment for One

By Melissa Glim

Wednesday, April 17, 2024

IRP researchers recently revealed that two different biological processes may cause alcohol-related liver damage.

While alcohol is a source of celebration and relaxation for many, it does come with significant drawbacks, especially when people over-consume it. For people who have trouble controlling their alcohol consumption — a condition called alcohol use disorder (AUD) — one of the most dangerous consequences can be damage to the liver, the organ that filters toxins like alcohol out of the blood.

In honor of Alcohol Awareness Month this April, I spoke with IRP senior investigator Bin Gao, M.D., Ph.D., about his quest to understand how AUD damages the liver and other organs by uncovering the molecules and mechanisms involved in that damage. His IRP lab also investigates how alcohol is processed in the body, producing insights that could be used to identify strategies for reducing alcohol consumption or reversing alcohol’s harmful effects.

Alcohol-associated liver disease is the leading cause of liver cancer and liver scarring, known as cirrhosis, which reduces the liver’s ability to filter our blood. Liver ailments caused by alcohol consumption account for almost half of all deaths from diseases of the liver and are rising dramatically. In fact, alcohol-associated liver disease increased by 23 percent in the first year of the COVID-19 pandemic. Yet even after five decades of study, there are no FDA-approved drugs available to treat alcohol-associated liver disease. The only cure is a liver transplant, but even that does not always work.

Heavy alcohol use can increase the accumulation of fat in the liver, leading to inflammation, fibrosis, and decreased liver function.

Much of Dr. Gao’s research focuses on understanding the mechanisms that lead to liver inflammation, referred to in medical circles as hepatitis, as well as how that inflammation subsequently damages the organ. In a recent study published in the Journal of Clinical Investigation in 2022, 1 Jing Ma, Ph.D., a postdoctoral fellow in Dr. Gao’s lab, identified two types of severe alcohol-associated hepatitis in both mice and tissue samples from liver transplant patients at Johns Hopkins Hospital in Baltimore, Maryland. Although all the patients had similar symptoms, one set of them had high levels of white blood cells called neutrophils in their livers and low levels of cells called CD8+ T cells, while the other group had the opposite balance of these cells in their livers.

“One of the major findings in this study was that the pattern of inflammatory cell infiltration is different from patient to patient,” Dr. Gao says. “This could also suggest they have different mechanisms leading to their liver failure."

To learn how these differences might influence the most appropriate treatment, Dr. Gao and his colleagues next extracted and sequenced RNA from individual cells in the damaged livers removed from severe alcohol-associated hepatitis patients receiving liver transplants at Johns Hopkins Hospital. The IRP scientists focused in particular on neutrophils in the livers, leading them to identify a group of liver neutrophils that had unique genetic profiles. Specifically, those neutrophils had a gene called CXCL8 , which codes for interleukin-8 (IL-8), a chemical that appears to be connected with the processes that lead to liver damage and, eventually, liver failure. The researchers suspect that drugs that block or inhibit the IL-8 made by those neutrophils may prove effective in stopping disease progression. 2

A neutrophil (green) engulfing bacteria (purple). Dr. Gao’s research suggests that in some people with alcohol-related liver disease, neutrophils turn against the body instead of defending it.

“I strongly believe that the neutrophil IL-8 target will work, maybe not in all patients, but at least in certain groups of patients,” Dr. Gao says. “I believe this is one of our most important findings, so we’re trying to work with physicians to start clinical trials.”

Dr. Gao is particularly excited about the ‘single-cell sequencing’ technique they used for that study because he believes it can be applied to other organs as well. This is important because the liver isn’t the only organ that AUD harms.

“I think people could use the same approach to study alcohol-associated pancreatitis because there is also a lot of neutrophil infiltration into the tissue in that disease,” he says.

In addition to identifying the molecular mechanisms underlying alcohol-associated liver damage, Dr. Gao is also very interested in understanding how our organs are affected by the way the body processes alcohol. While the liver is the primary tool used to process alcohol, it turns out that other organs are involved as well. Dr. Gao’s team learned this when they created a type of mouse in which the liver lacks aldehyde dehydrogenase-2, an important enzyme that the liver uses to expel the products of alcohol processing from the body. The IRP researchers expected to see evidence of alcohol processing and removal of the resulting toxic molecules from the body drop precipitously after drinking alcohol in those mice, but the drop was much smaller than anticipated.

Dr. Bin Gao

“If you read the scientific literature or textbooks, you’ll learn that the liver is responsible for processing about 98 percent of alcohol,” Dr. Gao says, “but when we treated those mice that lack alcohol-metabolizing enzymes in the liver, alcohol processing only dropped by about 30 percent. I thought this doesn’t make sense. It should be a huge drop.”

This surprising finding led Dr. Gao’s team to begin studying other organs to see how they contribute to alcohol processing in the body. The scientists found that in mice that lacked the gene for aldehyde dehydrogenase-2 in liver cells, other organs like the intestines and fat tissues pick up the slack for the hindered liver.

By revealing the intricacies of how various parts of the body interact with the alcohol we drink, research like Dr. Gao’s will move scientists closer to finding effective treatments for patients whose health is harmed by their alcohol consumption.

“Alcohol use disorder is a huge public health issue,” Dr. Gao says, “but I think we can find some ways to treat it and save patients’ lives.”

References:

[1] Ma J, Guillot A, Yang Z, Mackowiak B, Hwang S, Park O, Peiffer BJ, Ahmadi AR, Melo L, Kusumanchi P, Huda N, Saxena R, He Y, Guan Y, Feng D, Sancho-Bru P, Zang M, Cameron AM, Bataller R, T acke F, Sun Z, Liangpunsakul S, Gao B. Distinct histopathological phenotypes of severe alcoholic hepatitis suggest different mechanisms driving liver injury and failure. J Clin Invest. 2022; 132(14):e157780. doi: 10.1172/JCI157780.

[2] Guan Y, Peiffer B, Feng D, Parra MA, Wang Y, Fu Y, Shah VH, Cameron AM, Sun Z, Gao Bin. IL-8+ neutrophils drive inexorable inflammation in severe alcohol-associated hepatitis. J Clini Invest. In preparation. J Clin Invest . 2024 Mar 19:e178616. doi: 10.1172/JCI178616.

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• Published: 06 December 2022

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Advancing alcohol-related liver disease: from novel biomarkers to refining selection for liver transplantation

• Juan Pablo Arab   ORCID: orcid.org/0000-0002-8561-396X 1 , 2 &
• Ramon Bataller 3 , 4  

Nature Reviews Gastroenterology & Hepatology volume  20 ,  pages 71–72 ( 2023 ) Cite this article

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• Alcoholic liver disease
• Liver cirrhosis

Key advances

Non-invasive plasma proteomics from patients with alcohol-related liver disease (ALD) can be used to identify prognostic biomarkers and potential targets for therapy 5 .

The gut–liver axis has a key role in the pathophysiology of ALD; the complement receptor of immunoglobulin superfamily (CRIg) mediates pathogen recognition and phagocytic function in ALD and could represent a novel targeted therapy 7 .

Early liver transplantation for patients with severe alcohol-associated hepatitis not responding to medical therapy provides a significant survival benefit; although there is a slightly higher risk of relapse after liver transplantation, clinical outcomes at 2 years are similar in early versus standard liver transplantation indication in patients with ALD 9 .

In 2022, we witnessed advances in the field of alcohol-related liver disease. Key developments included the discovery of novel proteomics-based biomarkers and potential therapeutic targets that regulate the recognition of molecules derived from gut microbiota to modulate liver injury. Additionally, there have been significant advances in refining selection for liver transplantation in severe alcohol-associated hepatitis.

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Ayares, G. et al. Public health measures and prevention of alcohol-associated liver disease. J. Clin. Exp. Hepatol. 12 , 1480–1491 (2022).

Article   Google Scholar  

Griswold, M. G. et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 392 , 1015–1035 (2018).

Kwong, A. J. et al. OPTN/SRTR 2020 Annual Data Report: Liver. Am. J. Transplant 22 (Suppl. 2), 204–309 (2022).

Arab, J. P. et al. Identification of optimal therapeutic window for steroid use in severe alcohol-associated hepatitis: a worldwide study. J. Hepatol. 75 , 1026–1033 (2021).

Niu, L. et al. Noninvasive proteomic biomarkers for alcohol-related liver disease. Nat. Med. 28 , 1277–1287 (2022).

Article   CAS   Google Scholar  

Duan, Y. et al. Bacteriophage targeting of gut bacterium attenuates alcoholic liver disease. Nature 575 , 505–511 (2019).

Duan, Y. et al. CRIg on liver macrophages clears pathobionts and protects against alcoholic liver disease. Nat. Commun. 12 , 7172 (2021).

Mathurin, P. et al. Early liver transplantation for severe alcoholic hepatitis. N. Engl. J. Med. 365 , 1790–1800 (2011).

Louvet, A. et al. Early liver transplantation for severe alcohol-related hepatitis not responding to medical treatment: a prospective controlled study. Lancet Gastroenterol. Hepatol. 7 , 416–425 (2022).

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Division of Gastroenterology, Department of Medicine, Schulich School of Medicine, Western University & London Health Sciences Centre, London, Ontario, Canada

Juan Pablo Arab

Departamento de Gastroenterología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile

Liver Unit Hospital Clínic of Barcelona, Barcelona, Spain

Ramon Bataller

School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain

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Arab, J.P., Bataller, R. Advancing alcohol-related liver disease: from novel biomarkers to refining selection for liver transplantation. Nat Rev Gastroenterol Hepatol 20 , 71–72 (2023). https://doi.org/10.1038/s41575-022-00723-6

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DOI : https://doi.org/10.1038/s41575-022-00723-6

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