Assessing Effects of BHV-0223 40 mg Zydis Sublingual Formulation and Riluzole 50 mg Oral Tablet on Liver Function Test Parameters Utilizing DILIsym

A study on the pharmacokinetic bioequivalence of oral tablet formulations of riluzole among healthy volunteers utilizing HPLC-MS/MS

INTRODUCTION

This randomized, open-label, two period, two treatment, fasting bioequivalence trial was conducted to demonstrate the bioequivalence between riluzole tablets manufactured by Jiangsu Enhua Pharmaceutical Co., Ltd. and the reference preparations from Sanofi Winthrop Industry (certified by Sanofi Mature IP) in healthy individuals.

OBJECTIVE

The study aimed to compare the pharmacokinetic parameters and evaluate the bioequivalence of both preparations when taken on an empty stomach. Additionally, the safety profile of both preparations was assessed in the study population.

METHODS

Seventy-two subjects participated in the trial and received riluzole tablets once per dosing cycle while fasting. They were randomLy assigned to either a 50-mg test or reference formulation, with a 7-day washout period between cycles. Venous blood samples (4 mL) were collected 22 times from each subject, starting before dosing (0 h) and ending 48 h after. Plasma riluzole concentrations were measured using liquid chromatography tandem mass spectrometry. This clinical trial has been officially registered in the Chinese Clinical Trial Register (accessible at http://www.chinadrugtrials.org.cn/index.htmL ) with the registration number CTR20230637 on March 02, 2023.

RESULTS

The results showed that the geometric mean ratios of key pharmacokinetic parameters-including the area under the plasma concentration-time curve from time zero to the last nonzero concentration (AUC0 - t) (102.21%; confidence interval [CI], 96.85-107.86%), AUC from time zero to infinity (AUC0-∞) (102.03%; CI, 96.86-107.47%), and the peak plasma concentration (Cmax) (107.47%; CI, 95.03-121.54%)-all fell within the bioequivalence acceptance range of 80-125%. Importantly, no serious adverse events were reported, and no subjects withdrew due to adverse events, indicating good tolerability of both formulations among the healthy Chinese volunteers.

CONCLUSION

These findings establish the bioequivalence of the 50-mg test preparation of oral riluzole tablets with the reference listed drug.

Drug Induced Liver Injury: Highlights and Controversies in the 2023 Literature

Drug-induced liver injury (DILI) remains an active field of clinical research and investigation with more than 4700 publications appearing in 2023 relating to hepatotoxicity of all causes and injury patterns. As in years past, we have attempted to identify and summarize highlights and controversies from the past year's literature. Several new and novel therapeutic agents were approved by the US Food and Drug Administration (FDA) in 2023, a number of which were associated with significant hepatotoxicity. Updates in the diagnosis and management of DILI using causality scores as well as newer artificial intelligence-based methods were published. Details of newly established hepatotoxins as well as updated information on previously documented hepatotoxic drugs is presented. Significant updates in treatment of DILI were also included as well as reports related to global DILI registries.

Preserving mitochondrial homeostasis protects against drug-induced liver injury via inducing OPTN (optineurin)-dependent Mitophagy

Disruption of mitochondrial function is observed in multiple drug-induced liver injuries (DILIs), a significant global health threat. However, how the mitochondrial dysfunction occurs and whether maintain mitochondrial homeostasis is beneficial for DILIs remains unclear. Here, we show that defective mitophagy by OPTN (optineurin) ablation causes disrupted mitochondrial homeostasis and aggravates hepatocytes necrosis in DILIs, while OPTN overexpression protects against DILI depending on its mitophagic function. Notably, mass spectrometry analysis identifies a new mitochondrial substrate, GCDH (glutaryl-CoA dehydrogenase), which can be selectively recruited by OPTN for mitophagic degradation, and a new cofactor, VCP (valosin containing protein) that interacts with OPTN to stabilize BECN1 during phagophore assembly, thus boosting OPTN-mediated mitophagy initiation to clear damaged mitochondria and preserve mitochondrial homeostasis in DILIs. Then, the accumulation of OPTN in different DILIs is further validated with a protective effect, and pyridoxine is screened and established to alleviate DILIs by inducing OPTN-mediated mitophagy. Collectively, our findings uncover a dual role of OPTN in mitophagy initiation and implicate the preservation of mitochondrial homeostasis via inducing OPTN-mediated mitophagy as a potential therapeutic approach for DILIs.Abbreviation: AILI: acetaminophen-induced liver injury; ALS: amyotrophic lateral sclerosis; APAP: acetaminophen; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CHX: cycloheximide; Co-IP: co-immunoprecipitation; DILI: drug-induced liver injury; FL: full length; GCDH: glutaryl-CoA dehydrogenase; GOT1/AST: glutamic-oxaloacetic transaminase 1; GO: gene ontology; GSEA: gene set enrichment analysis; GPT/ALT: glutamic - pyruvic transaminase; INH: isoniazid; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MMP: mitochondrial membrane potential; MST: microscale thermophoresis; MT-CO2/COX-II: mitochondrially encoded cytochrome c oxidase II; OPTN: optineurin; PINK1: PTEN induced kinase 1; PRKN: parkin RBR E3 ubiquitin protein ligase; TIMM23: translocase of inner mitochondrial membrane 23; TOMM20: translocase of outer mitochondrial membrane 20; TSN: toosendanin; VCP: valosin containing protein, WIPI2: WD repeat domain, phosphoinositide interacting 2.

Comparison of drug-induced liver injury risk between propylthiouracil and methimazole: A quantitative systems toxicology approach

Propylthiouracil (PTU) and methimazole (MMI), two classical antithyroid agents possess risk of drug-induced liver injury (DILI) with unknown mechanism of action. This study aimed to examine and compare their hepatic toxicity using a quantitative system toxicology approach. The impact of PTU and MMI on hepatocyte survival, oxidative stress, mitochondrial function and bile acid transporters were assessed in vitro. The physiologically based pharmacokinetic (PBPK) models of PTU and MMI were constructed while their risk of DILI was calculated by DILIsym, a quantitative systems toxicology (QST) model by integrating the results from in vitro toxicological studies and PBPK models. The simulated DILI (ALT >2 × ULN) incidence for PTU (300 mg/d) was 21.2%, which was within the range observed in clinical practice. Moreover, a threshold dose of 200 mg/d was predicted with oxidative stress proposed as an important toxic mechanism. However, DILIsym predicted a 0% incidence of hepatoxicity caused by MMI (30 mg/d), suggesting that the toxicity of MMI was not mediated through mechanism incorporated into DILIsym. In conclusion, DILIsym appears to be a practical tool to unveil hepatoxicity mechanism and predict clinical risk of DILI.

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 modelling 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.

Development and Evaluation of Amorphous Solid Dispersion of Riluzole with PBPK Model to Simulate the Pharmacokinetic Profile

In the current work, screening of polymers viz. polyacrylic acid (PAA), polyvinyl pyrrolidone vinyl acetate (PVP VA), and hydroxypropyl methyl cellulose acetate succinate (HPMC AS) based on drug-polymer interaction and wetting property was done for the production of a stable amorphous solid dispersion (ASD) of a poorly water-soluble drug Riluzole (RLZ). PAA showed maximum interaction and wetting property hence, was selected for further studies. Solid state characterization studies confirmed the formation of ASD with PAA. Saturation solubility, dissolution profile, and in vivo pharmacokinetic data of the ASD formulation were generated in rats against its marketed tablet Rilutor. The RLZ:PAA ASD showed exponential enhancement in the dissolution of RLZ. Predicted and observed pharmacokinetic data in rats showed enhanced area under curve (AUC) and Cmax in plasma and brain with respect to Rilutor. Furthermore, a physiologically based pharmacokinetic (PBPK) model of rats for Rilutor and RLZ ASD was developed and then extrapolated to humans where physiological parameters were changed along with a biochemical parameter. The partition coefficient was kept similar in both species. The model was used to predict different exposure scenarios, and the simulated data was compared with observed data points. The PBPK model simulated Cmax and AUC was within two times the experimental data for plasma and brain. The Cmax and AUC in the brain increased with ASD compared to Rilutor for humans showing its potential in improving its biopharmaceutical performance and hence enhanced therapeutic efficacy. The model can predict the RLZ concentration in multiple compartments including plasma and liver.

Considerations for Physiologically Based Modeling in Liver Disease: From Nonalcoholic Fatty Liver (NAFL) to Nonalcoholic Steatohepatitis (NASH)

Nonalcoholic fatty liver disease (NAFLD), representing a clinical spectrum ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), is rapidly evolving into a global pandemic. Patients with NAFLD are burdened with high rates of metabolic syndrome-related comorbidities resulting in polypharmacy. Therefore, it is crucial to gain a better understanding of NAFLD-mediated changes in drug disposition and efficacy/toxicity. Despite extensive clinical pharmacokinetic data in cirrhosis, current knowledge concerning pharmacokinetic alterations in NAFLD, particularly at different stages of disease progression, is relatively limited. In vitro-to-in vivo extrapolation coupled with physiologically based pharmacokinetic and pharmacodynamic (IVIVE-PBPK/PD) modeling offers a promising approach for optimizing pharmacologic predictions while refining and reducing clinical studies in this population. Use of IVIVE-PBPK to predict intra-organ drug concentrations at pharmacologically relevant sites of action is particularly advantageous when it can be linked to pharmacodynamic effects. Quantitative systems pharmacology/toxicology (QSP/QST) modeling can be used to translate pharmacokinetic and pharmacodynamic data from PBPK/PD models into clinically relevant predictions of drug response and toxicity. In this review, a detailed summary of NAFLD-mediated alterations in human physiology relevant to drug absorption, distribution, metabolism, and excretion (ADME) is provided. The application of literature-derived physiologic parameters and ADME-associated protein abundance data to inform virtual NAFLD population development and facilitate PBPK/PD, QSP, and QST predictions is discussed along with current limitations of these methodologies and knowledge gaps. The proposed methodologic framework offers great potential for meaningful prediction of pharmacological outcomes in patients with NAFLD and can inform both drug development and clinical practice for this population.

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.

Quantitative Systems Toxicology and Drug Development: The DILIsym Experience

DILIsym^® is a Quantitative Systems Toxicology (QST) model that has been developed over the last decade by a public-private partnership to predict the liver safety liability in new drug candidates. DILIsym integrates the quantitative abilities of parent and relevant metabolites to cause oxidative stress, mitochondrial dysfunction, and alter bile acid homeostasis. Like the prediction of drug-drug interactions, the data entered into DILIsym are assessed in the laboratory in human experimental systems, and combined with estimates of liver exposure to predict the outcome. DILIsym is now frequently used in decision-making within the pharmaceutical industry and its modeling results are increasingly included in regulatory communications and NDA submissions. DILIsym can be used to identify dominant mechanisms underlying liver toxicity and this information is increasingly being used to identify patient-specific risk factors, including certain disease states. DILIsym is also increasingly used to optimize the interpretation of liver injury biomarkers. DILIsym provides an example of how QST modeling can help speed the delivery of safer new drugs to the patients who need them.