The role of DMPK science in improving pharmaceutical research and development efficiency

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.

Current and emerging approaches to noncompetitive AR inhibition

The androgen receptor (AR) has been shown to be a key determinant in the pathogenesis of castration-resistant prostate cancer (CRPC). The current standard of care therapies targets the ligand-binding domain of the receptor and can afford improvements to life expectancy often only in the order of months before resistance occurs. Emerging preclinical and clinical compounds that inhibit receptor activity via differentiated mechanisms of action which are orthogonal to current antiandrogens show promise for overcoming treatment resistance. In this review, we present an authoritative summary of molecules that noncompetitively target the AR. Emerging small molecule strategies for targeting alternative domains of the AR represent a promising area of research that shows significant potential for future therapies. The overall quality of lead candidates in the area of noncompetitive AR inhibition is discussed, and it identifies the key chemotypes and associated properties which are likely to be, or are currently, positioned to be first in human applications.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning

MOTIVATION

Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability.

RESULTS

To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization.

AVAILABILITY AND IMPLEMENTATION

The code and data underlying this article are freely available at https://github.com/dubingxue/CMMS-GCL.

High-Throughput Assay of Cytochrome P450-Dependent Drug Demethylation Reactions and Its Use to Re-Evaluate the Pathways of Ketamine Metabolism

In a search for a reliable, inexpensive, and versatile technique for high-throughput kinetic assays of drug metabolism, we elected to rehire an old-school approach based on the determination of formaldehyde (FA) formed in cytochrome P450-dependent demethylation reactions. After evaluating several fluorometric techniques for FA detection, we chose the method based on the Hantzsch reaction with acetoacetanilide as the most sensitive, robust, and adaptable to high-throughput implementation. Here we provide a detailed protocol for using our new technique for automatized assays of cytochrome P450-dependent drug demethylations and discuss its applicability for high-throughput scanning of drug metabolism pathways in the human liver. To probe our method further, we applied it to re-evaluating the pathways of metabolism of ketamine, a dissociative anesthetic and potent antidepressant increasingly used in the treatment of alcohol withdrawal syndrome. Probing the kinetic parameters of ketamine demethylation by ten major cytochrome P450 (CYP) enzymes, we demonstrate that in addition to CYP2B6 and CYP3A enzymes, which were initially recognized as the primary metabolizers of ketamine, an important role is also played by CYP2C19 and CYP2D6. At the same time, the involvement of CYP2C9 suggested in the previous reports was deemed insignificant.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Anti-Inflammatory Effects of Auranamide and Patriscabratine-Mechanisms and In Silico Studies

Auranamide and patriscabratine are amides from Melastoma malabathricum (L.) Smith. Their anti-inflammatory activity and nuclear factor erythroid 2-related factor 2 (NRF2) activation ability were evaluated using Escherichia coli lipopolysaccharide (LPSEc)-stimulated murine macrophages (RAW264.7) and murine hepatoma (Hepa-1c1c7) cells, respectively. The cytotoxicity of the compounds was assessed using a 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assay. The anti-inflammatory activity was determined by measuring the nitric oxide (NO) production and pro-inflammatory cytokines (Interleukin (IL)-1β, Interferon (IFN)-γ, tumour necrosis factor (TNF)-α, and IL-6) and mediators (NF-κB and COX-2). NRF2 activation was determined by measuring the nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) quinone oxidoreductase 1 (NQO1), nuclear NRF2 and hemeoxygenase (HO)-1. In vitro metabolic stability was assessed using the mouse, rat, and human liver microsomes. The compounds were non-toxic to the cells at 10 μM. Both compounds showed dose-dependent effects in downregulating NO production and pro-inflammatory cytokines and mediators. The compounds also showed upregulation of NQO1 activity and nuclear NRF2 and HO-1 levels. The compounds were metabolically stable in mouse, rat and human liver microsomes. The possible molecular targets of NRF2 activation by these two compounds were predicted using molecular docking studies and it was found that the compounds might inhibit the Kelch domain of KEAP1 and GSK-3β activity. The physicochemical and drug-like properties of the test compounds were predicted using Schrodinger small molecule drug discovery suite (v.2022-2).