Mechanistic Investigations Support Liver Safety of Ubrogepant

Prediction of the Liver Safety Profile of a First-in-Class Myeloperoxidase Inhibitor Using Quantitative Systems Toxicology Modeling

The novel myeloperoxidase inhibitor verdiperstat was developed as a treatment for neuroinflammatory and neurodegenerative diseases. During development, a computational prediction of verdiperstat liver safety was performed using DILIsym v8A, a quantitative systems toxicology (QST) model of liver safety.A physiologically-based pharmacokinetic (PBPK) model of verdiperstat was constructed in GastroPlus 9.8, and outputs for liver and plasma time courses of verdiperstat were input into DILIsym. In vitro experiments measured the likelihood that verdiperstat would inhibit mitochondrial function, inhibit bile acid transporters, and generate reactive oxygen species (ROS); these results were used as inputs into DILIsym, with two alternate sets of parameters used in order to fully explore the sensitivity of model predictions. Verdiperstat dosing protocols up to 600 mg BID were simulated for up to 48 weeks using a simulated population (SimPops) in DILIsym.Verdiperstat was predicted to be safe, with only very rare, mild liver enzyme increases as a potential possibility in highly sensitive individuals. Subsequent Phase 3 clinical trials found that ALT elevations in the verdiperstat treatment group were generally similar to those in the placebo group. This validates the DILIsym simulation results and demonstrates the power of QST modeling to predict the liver safety profile of novel therapeutics.

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.

Quantitative Systems Toxicology Identifies Independent Mechanisms for Hepatotoxicity and Bilirubin Elevations Due to AKR1C3 Inhibitor BAY1128688

BAY1128688 is a selective inhibitor of AKR1C3, investigated recently in a trial that was prematurely terminated due to drug-induced liver injury. These unexpected observations prompted use of the quantitative systems toxicology model, DILIsym, to determine possible mechanisms of hepatotoxicity. Using mechanistic in vitro toxicity data as well as clinical exposure data, DILIsym predicted the potential for BAY1128688 to cause liver toxicity (elevations in serum alanine aminotransferase (ALT)) and elevations in serum bilirubin. Initial simulations overpredicted hepatotoxicity and bilirubin elevations, so the BAY1128688 representation within DILIsym underwent optimization. The liver partition coefficient Kp was altered to align simulated bilirubin elevations with those observed clinically. Altering the mode of bile acid canalicular and basolateral efflux inhibition was necessary to accurately predict ALT elevations. Optimization results support that bilirubin elevations observed early during treatment are due to altered bilirubin metabolism and transporter inhibition, which is independent of liver injury. The modeling further supports that on-treatment ALT elevations result from inhibition of bile acid transporters, particularly the bile salt excretory pump, leading to accumulation of toxic bile acids. The predicted dose-dependent intrinsic hepatotoxicity may increase patient susceptibility to an adaptive immune response, accounting for ALT elevations observed after completion of treatment. These BAY1128688 simulations provide insight into the mechanisms behind hepatotoxicity and bilirubin elevations and may inform the potential risk posed by future compounds.

The Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury

Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.

Narrative review of migraine management in patients with renal or hepatic disease

OBJECTIVES/BACKGROUND

Treatment of migraine in the setting of either renal or hepatic disease can be daunting for clinicians. Not only does the method of metabolism have to be considered, but also the method of elimination/excretion of the parent drug and any active or toxic metabolites. Furthermore, it is difficult to think about liver or kidney disease in isolation, as liver disease can sometimes contribute to impaired renal function and renal disease can sometimes impair hepatic metabolism, through the cytochrome P450 system.

METHODS

A detailed search for terms related to liver disease, renal disease, and migraine management was performed in PubMed, Ovid Medline, Embase, and the Cochrane Library.For each medication, product labels were retrieved and reviewed using the US FDA website, with additional review of IBM Micromedex, LiverTox, and the Renal Drug Handbook.

RESULTS

This manuscript provides an overview of migraine drug metabolism and how it can be affected by liver and renal impairment. It reviews the standard terminology recommended by the US Food and Drug Administration for the different stages of hepatic and renal failure. The available evidence regarding the use of abortive and preventative medicines in the setting of organ failure is discussed in detail, including more recent therapies such as lasmiditan, gepants, and calcitonin gene-related peptide antibodies.

CONCLUSIONS

For acute therapy, the use of NSAIDS should be limited, as these carry risk for both severe hepatic and renal disease. Triptans can be selectively used, often with dose guideline adjustments. Ubrogepant may be used in severe hepatic disease with dose adjustment and lasmiditan can be used in end stage renal disease. Though non-medicine strategies may be the most reasonable initial approach, many preventative medications can be used in the setting of hepatic and renal disease, often with dose adjustment. This review provides tables of guidelines, including reduced dosing recommendations, for the use of abortive and preventative migraine medications in hepatic and renal failure.

Applications of In Silico Models to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

New Generation Gepants: Migraine Acute and Preventive Medications

Migraine is a debilitating disease whose clinical and social impact is out of debate. Tolerability issues, interactions, contraindications, and inefficacy of the available medications make new options necessary. The calcitonin-gene-related peptide (CGRP) pathway has shown its importance in migraine pathophysiology and specific medications targeting this have become available. The first-generation CGRP receptor antagonists or gepants, have undergone clinical trials but their development was stopped because of hepatotoxicity. The new generation of gepants, however, are efficacious, safe, and well tolerated as per recent clinical trials. This led to the FDA-approval of rimegepant, ubrogepant, and atogepant. The clinical trials of the available gepants and some of the newer CGRP-antagonists are reviewed in this article.

Drug-Induced Liver Injury: Highlights and Controversies in the Recent Literature

Drug-induced liver injury (DILI) remains an important, yet challenging diagnosis for physicians. Each year, additional drugs are implicated in DILI and this year was no different, with more than 1400 articles published on the subject. This review examines some of the most significant highlights and controversies in DILI-related research over the past year and their implications for clinical practice. Several new drugs were approved by the US Food and Drug Administration including a number of drugs implicated in causing DILI, particularly among the chemotherapeutic classes. The COVID-19 pandemic was also a major focus of attention in 2020 and we discuss some of the notable aspects of COVID-19-related liver injury and its implications for diagnosing DILI. Updates in diagnostic and causality assessments related to DILI such as the Roussel Uclaf Causality Assessment Method are included, mindful that there is still no single biomarker or diagnostic tool to unequivocally diagnose DILI. Glutamate dehydrogenase received renewed attention as being more specific than alanine aminotransferase. There were a few new reports of previously unrecognized hepatotoxins, including immune modulators and novel gene therapy drugs that we highlight. Updates and new developments of previously described hepatotoxins, such as immune checkpoint inhibitors and anti-tuberculosis drugs are reviewed. Finally, novel technologies such as organoid culture systems to better predict DILI preclinically may be coming of age and determinants of hepatocyte loss, such as calculating P_ALT are poised to improve our current means of estimating DILI severity and the risk of acute liver failure.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Atogepant Is Not Associated With Clinically Meaningful Alanine Aminotransferase Elevations in Healthy Adults

Atogepant is a potent, selective, oral calcitonin gene–related peptide (CGRP) receptor antagonist in development for migraine prevention. The chemical structure of atogepant is distinct from previous CGRP receptor antagonists, which were associated with elevated serum alanine aminotransferase (ALT) in clinical trials. Here, we report the safety, tolerability, and pharmacokinetics (PKs) of a once‐daily supratherapeutic dose (170 mg) of atogepant for 28 days from a randomized, double‐blind, placebo‐controlled phase I trial in healthy participants. Overall safety, hepatic safety, and plasma PK parameters were evaluated. Thirty‐four participants aged 23–55 years enrolled; 28 (82.4%) completed the study in accordance with the protocol. Multiple doses of 170 mg atogepant for 28 consecutive days were generally well‐tolerated. All adverse events (AEs; reported in 87.0% of the atogepant group; 72.7%, placebo) were mild in severity except one serious AE of subarachnoid hemorrhage due to a bicycle accident and not considered related to treatment. There were two discontinuations due to AEs, both with atogepant, one considered possibly related to treatment. Over 28 days of treatment, no participant receiving atogepant had an ALT elevation above 1.5 × upper limit of normal. Change from baseline in serum ALT levels was not different between atogepant and placebo. Atogepant is rapidly absorbed (median time to maximum plasma concentration, ~ 2 hours) with an apparent terminal half‐life of ~ 11 hours, and no evidence of accumulation after once‐daily dosing. Overall, atogepant at a high oral dose is safe and well‐tolerated in healthy participants with no clinically meaningful elevations in ALT.