A novel orally available small molecule that inhibits hepatitis B virus expression.

BACKGROUND & AIMS: The hallmarks of chronic HBV infection are a high viral load (HBV DNA) and even higher levels (>100-fold in excess of virions) of non-infectious membranous particles containing the tolerogenic viral S antigen (HBsAg). Currently, standard treatment effectively reduces viremia but only rarely results in a functional cure (defined as sustained HBsAg loss). There is an urgent need to identify novel therapies that reduce HBsAg levels and restore virus-specific immune responsiveness in patients. We report the discovery of a novel, potent and orally bioavailable small molecule inhibitor of HBV gene expression (RG7834). METHODS: RG7834 antiviral characteristics and selectivity against HBV were evaluated in HBV natural infection assays and in a urokinase-type plasminogen activator/severe combined immunodeficiency humanized mouse model of HBV infection, either alone or in combination with entecavir. RESULTS: Unlike nucleos(t)ide therapies, which reduce viremia but do not lead to an effective reduction in HBV antigen expression, RG7834 significantly reduced the levels of viral proteins (including HBsAg), as well as lowering viremia. Consistent with its proposed mechanism of action, time course RNA-seq analysis revealed a fast and selective reduction in HBV mRNAs in response to RG7834 treatment. Furthermore, oral treatment of HBV-infected humanized mice with RG7834 led to a mean HBsAg reduction of 1.09 log10 compared to entecavir, which had no significant effect on HBsAg levels. Combination of RG7834, entecavir and pegylated interferon α-2a led to significant reductions of both HBV DNA and HBsAg levels in humanized mice. CONCLUSION: We have identified a novel oral HBV viral gene expression inhibitor that blocks viral antigen and virion production, that is highly selective for HBV, and has a unique antiviral profile that is clearly differentiated from nucleos(t)ide analogues. LAY SUMMARY: We discovered a novel small molecule viral expression inhibitor that is highly selective for HBV and unlike current therapy inhibits the expression of viral proteins by specifically reducing HBV mRNAs. RG7834 can therefore potentially provide anti-HBV benefits and increase HBV cure rates, by direct reduction of viral agents needed to complete the viral life cycle, as well as a reduction of viral agents involved in evasion of the host immune responses.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Investigating Tacrolimus Disposition in Paediatric Patients with a Physiologically Based Pharmacokinetic Model Incorporating CYP3A4 Ontogeny, Mechanistic Absorption and Red Blood Cell Binding.

Tacrolimus is a crucial immunosuppressant for organ transplant patients, requiring therapeutic drug monitoring due to its variable exposure after oral intake. Physiologically based pharmacokinetic (PBPK) modelling has provided insights into tacrolimus disposition in adults but has limited application in paediatrics. This study investigated age dependency in tacrolimus exposure at the levels of absorption, metabolism, and distribution. Based on the literature data, a PBPK model was developed to predict tacrolimus exposure in adults after intravenous and oral administration. This model was then extrapolated to the paediatric population, using a unique reference dataset of kidney transplant patients. Selecting adequate ontogeny profiles for hepatic and intestinal CYP3A4 appeared critical to using the model in children. The best model performance was achieved by using the Upreti ontogeny in both the liver and intestines. To mechanistically evaluate the impact of absorption on tacrolimus exposure, biorelevant in vitro solubility and dissolution data were obtained. A relatively fast and complete release of tacrolimus from its amorphous formulation was observed when mimicking adult or paediatric dissolution conditions (dose, fluid volume). In both the adult and paediatric PBPK models, the in vitro dissolution profiles could be adequately substituted by diffusion-layer-based dissolution modelling. At the level of distribution, sensitivity analysis suggested that differences in blood plasma partitioning of tacrolimus may contribute to the variability in exposure in paediatric patients.

Regulatory utility of physiologically based pharmacokinetic modeling for assessing food impact in bioequivalence studies: A workshop summary report.

This workshop report summarizes the presentations and panel discussion related to the use of physiologically based pharmacokinetic (PBPK) modeling approaches for food effect assessment, collected from Session 2 of Day 2 of the workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches." The US Food and Drug Administration in collaboration with the Center for Research on Complex Generics organized this workshop where this particular session titled "Oral PBPK for Evaluating the Impact of Food on BE" presented successful cases of PBPK modeling approaches for food effect assessment. Recently, PBPK modeling has started to gain popularity among academia, industries, and regulatory agencies for its potential utility during bioavailability (BA) and/or bioequivalence (BE) studies of new and generic drug products to assess the impact of food on BA/BE. Considering the promises of PBPK modeling in generic drug development, the aim of this workshop session was to facilitate knowledge sharing among academia, industries, and regulatory agencies to understand the knowledge gap and guide the path forward. This report collects and summarizes the information presented and discussed during this session to disseminate the information into a broader audience for further advancement in this area.