Characterization and Applications of Permeabilized Hepatocytes in Drug Discovery.

Hepatocytes are one of the most physiologically relevant in vitro liver systems for human translation of clearance and drug-drug interactions (DDI). However, the cell membranes of hepatocytes can limit the entry of certain compounds into the cells for metabolism and DDI. Passive permeability through hepatocytes can be different in vitro and in vivo, which complicates the human translation.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes.

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL.

Use of high throughput ion channel profiling and statistical modeling to predict off-target arrhythmia risk - One pharma's experience and perspective.

Introduction: The use of high throughput patch clamp profiling to determine mixed ion channel-mediated arrhythmia risk was assessed using profiling data generated using proprietary internal and clinical reference compounds. We define the reproducibility of the platform and highlight inherent platform issues. The data generated was used to develop predictive models for cardiac arrhythmia risk, specifically Torsades de Pointes (TdP).

Early Drug-Induced Liver Injury Risk Screening: "Free," as Good as It Gets.

For all the promise of and need for clinical drug-induced liver injury (DILI) risk screening systems, demonstrating the predictive value of these systems versus readily available physicochemical properties and inherent dosing information has not been thoroughly evaluated. Therefore, we utilized a systematic approach to evaluate the predictive value of in vitro safety assays including bile salt export pump transporter inhibition and cytotoxicity in HepG2 and transformed human liver epithelial along with physicochemical properties. We also evaluated the predictive value of in vitro ADME assays including hepatic partition coefficient (Kp) and its unbound counterpart because they provide insight on hepatic accumulation potential.

The Impact of a Breastfeeding Promotion Intervention on Rapid Weight Gain among African American Infants.

Background/objective: To evaluate the impact of exclusive breastfeeding (EBF) on rapid weight gain (RWG) among infants of African American women enrolled in a breastfeeding promotion intervention.

Methods: African American mothers in the 2nd or 3rd trimester who consented and attended four 30-minute breastfeeding promotion sessions prospectively provided breastfeeding and physical measurements at birth, four-, six-, and twelve-months.

Results: Mean age of mothers was 28.

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods.

Volume of distribution at steady state (V) is an important pharmacokinetic parameter of a drug candidate. In this study, V prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods.

Mechanistic insights on clearance and inhibition discordance between liver microsomes and hepatocytes when clearance in liver microsomes is higher than in hepatocytes.

Human liver microsomes (HLM) and human hepatocytes (HHEP) are two common in vitro systems used in metabolic stability and inhibition studies. The comparison between the assays using the two systems can provide mechanistic insights on the interplay of metabolism, passive permeability and transporters. This study investigated the critical factors impacting the unbound intrinsic clearance (CL) and IC of CYP3A inhibition between HLM and HHEP.

A Physiologically Based Tool to Assess the Risk of Drug-Related Crystalluria.

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney.

Structural attributes influencing unbound tissue distribution.

Unbound tissue-to-plasma partition coefficients (K) were determined for 56 structurally diverse compounds in rats following intravenous infusion. Five tissues were included in the study: white adipose, brain, heart, liver, and skeletal muscle. The rank ordering of the median tissue K values was: liver (4.

Evaluation of Fraction Unbound Across 7 Tissues of 5 Species.

Binding to various tissues and species is frequently assessed in drug discovery and development to support safety and efficacy studies. To reduce time, cost, and labor requirements for binding experiments, we conducted a large comparison study to evaluate the correlation of fraction unbound (f) across 7 tissues of 5 species, including white adipose, brain, heart, kidney, liver, lung, and skeletal muscle of mouse, rat, dog, monkey, and human. The results showed that there were no significant species differences of f for tissue binding, and a single-species (e.

Prediction of Fraction Unbound in Microsomal and Hepatocyte Incubations: A Comparison of Methods across Industry Datasets.

The fraction unbound in the incubation, , is an important parameter to consider in the evaluation of intrinsic clearance measurements performed in hepatocytes or microsomes. Reliable estimates of based on a compound's structure have the potential to positively impact the screening timelines in drug discovery. Previous works suggested that is primarily driven by passive processes and can be described using physicochemical properties such as lipophilicity and the protonation state of the molecule.

In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints.

Matched molecular pair analysis (MMPA) has emerged as a powerful approach to mine and extract tacit knowledge from measured databases of small molecules. Extracted knowledge from past experimentation can assist future lead optimization as an idea generation tool and, hence, reduce the number of design-synthesis-test cycles. While attractive and intuitive, MMPA still presents several limitations.

The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction.

Matched molecular series (MMS) analysis is an extension of matched molecular pair (MMP) analysis where all of the MMPs belong to the same chemical series. An MMS within a biological assay is able to capture specific structure activity relationships resulting from chemical substitution at a single location in the molecule. Under this convention, an MMS has the ability to capture one specific interaction vector between the compounds in a series and their therapeutic target.

In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development.

In silico tools to investigate absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK) properties of new chemical entities are an integral part of the current industrial drug discovery paradigm. While many companies are active in the field, scientists engaged in this area do not necessarily share the same background and have limited resources when seeking guidance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial setting. This work summarizes the views of a group of industrial in silico and experimental ADME scientists, participating in the In Silico ADME Working Group, a subgroup of the International Consortium for Innovation through Quality in Pharmaceutical Development (IQ) Drug Metabolism Leadership Group.

A multi-endpoint matched molecular pair (MMP) analysis of 6-membered heterocycles.

Aromatic rings, ubiquitous in pharmaceutical compounds, are often exchanged with another ring during the optimization process of drug discovery. Inevitably, the preferred ring system for one endpoint may prove detrimental to another, thus necessitating a holistic, multiple endpoint optimization approach for finding the ideal replacement. Accordingly, we conducted an extensive matched molecular pair (MMP) analysis of common 6-membered aromatic rings across 4 endpoints critical for drug discovery (logD lipophilicity, microsomal metabolism, P-gp efflux and passive permeability).

Escape from neutralization by the respiratory syncytial virus-specific neutralizing monoclonal antibody palivizumab is driven by changes in on-rate of binding to the fusion protein.

The role of binding kinetics in determining neutralizing potency for antiviral antibodies is poorly understood. While it is believed that increased steady-state affinity correlates positively with increased virus-neutralizing activity, the relationship between association or dissociation rate and neutralization potency is unclear. We investigated the effect of naturally-occurring antibody resistance mutations in the RSV F protein on the kinetics of binding to palivizumab.

Evaluating the differences in cycloalkyl ether metabolism using the design parameter "lipophilic metabolism efficiency" (LipMetE) and a matched molecular pairs analysis.

We have observed previously that modification of ring size and substitution pattern may be used as a strategy to mitigate the metabolic instability of cycloalkyl ethers. In this article, we introduce a medicinal chemistry design parameter named "lipophilic metabolism efficiency" (LipMetE) that indicates that these changes in metabolic stability can be largely ascribed to changes in lipophilicity. Our matched molecular pair analysis also indicates that this finding is a general phenomenon, widely observed across different chemotypes.

Reversion of somatic mutations of the respiratory syncytial virus-specific human monoclonal antibody Fab19 reveal a direct relationship between association rate and neutralizing potency.

The role of affinity in determining neutralizing potency of mAbs directed against viruses is not well understood. We investigated the kinetic, structural, and functional advantage conferred by individual naturally occurring somatic mutations in the Ab H chain V region of Fab19, a well-described neutralizing human mAb directed to respiratory syncytial virus. Comparison of the affinity-matured Ab Fab19 with recombinant Fab19 Abs that were variants containing reverted amino acids from the inferred unmutated ancestor sequence revealed the molecular basis for affinity maturation of this Ab.

Interpretable, probability-based confidence metric for continuous quantitative structure-activity relationship models.

A great deal of research has gone into the development of robust confidence in prediction and applicability domain (AD) measures for quantitative structure-activity relationship (QSAR) models in recent years. Much of the attention has historically focused on structural similarity, which can be defined in many forms and flavors. A concept that is frequently overlooked in the realm of the QSAR applicability domain is how the local activity landscape plays a role in how accurate a prediction is or is not.

Mechanistic insights from comparing intrinsic clearance values between human liver microsomes and hepatocytes to guide drug design.

Metabolic stability of drug candidates are often determined in both liver microsome and hepatocyte assays. Comparison of intrinsic clearance values between the two assays provides additional information to guide drug design. Intrinsic clearance values from human liver microsomes and hepatocytes were compared for a set of commercial drugs with known metabolic pathways and transporter characteristics.

Systematic and pairwise analysis of the effects of aromatic halogenation and trifluoromethyl substitution on human liver microsomal clearance.

Fluorine- and chlorine-containing moieties have been strategically integrated into chemical structures to optimize the pharmacokinetic and metabolic properties of therapeutic agents, based partly on the concept that the addition of these substituents may lower microsomal clearance. A large-scale systematic mechanistic study of drug metabolic alteration by aromatic halogenation has hitherto not been possible due to the lack of either large clearance databases or adequate data mining tools. To address this, we systematically searched compound pairs in Pfizer's human liver microsomal clearance database of over 220,000 unique compounds to assess the effects of fluoro-, chloro- and trifluoromethyl-substitution on phenyl derivatives.

Extraction of tacit knowledge from large ADME data sets via pairwise analysis.

Pharmaceutical companies routinely collect data across multiple projects for common ADME endpoints. Although at the time of collection the data is intended for use in decision making within a specific project, knowledge can be gained by data mining the entire cross-project data set for patterns of structure-activity relationships (SAR) that may be applied to any project. One such data mining method is pairwise analysis.

Neutralizing antibodies derived from the B cells of 1918 influenza pandemic survivors.

Investigation of the human antibody response to influenza virus infection has been largely limited to serology, with relatively little analysis at the molecular level. The 1918 H1N1 influenza virus pandemic was the most severe of the modern era. Recent work has recovered the gene sequences of this unusual strain, so that the 1918 pandemic virus could be reconstituted to display its unique virulence phenotypes.