Bioactivation and reactivity research advances-2023 year in review.

Advances in the field of bioactivation have significantly contributed to our understanding and prediction of drug-induced liver injury (DILI). It has been established that many adverse drug reactions, including DILI, are associated with the formation and reactivity of metabolites. Modern methods allow us to detect and characterize these reactive metabolites in earlier stages of drug development, which helps anticipate and circumvent the potential for DILI. Improved in silico models and experimental techniques that better reflect in vivo environments are enhancing predictive capabilities for DILI risk. Further, studies on the mechanisms of bioactivation, including enzyme interactions and the role of individual genetic differences, have provided valuable insights for drug optimizations. Cumulatively, this progress is continually refining our approaches to drug safety evaluation and personalized medicine.Shuai Wang and Cyrus Khojasteh, on behalf of the authors.

Biotransformation research advances - 2022 year in review.

This annual review is the eighth of its kind since 2016 (Baillie et al. 2016, Khojasteh et al. 2017, Khojasteh et al. 2018, Khojasteh et al. 2019, Khojasteh et al. 2020, Khojasteh et al. 2021, Khojasteh et al. 2022). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation.

Bioactivation and reactivity research advances - 2022 year in review‡.

With the 50th year mark since the launch of Drug Metabolism and Disposition journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023).This has, in a major part, been due to the continued advances across the whole spectrum of applied technologies in hardware, software, machine learning (ML), and artificial intelligence (AI). LC-MS platforms continue to evolve to support key applications in the field, and automation is also improving the accuracy, precision, and throughput of these supporting assays. In addition, sample generation and processing is being aided by increased diversity and quality of reagents and bio-matrices so that what is being analyzed is more relevant and translatable. The application of in silico platforms (applied software, ML, and AI) is also making great strides, and in tandem with the more traditional approaches mentioned previously, is significantly advancing our understanding of bioactivation pathways and how these play a role in toxicity. All of this continues to allow the area of bioactivation to evolve in parallel with associated fields to help bring novel or improved medicines to patients with urgent or unmet needs.Shuai Wang and Cyrus Khojasteh, on behalf of the authors.

Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes.

Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug-drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.

Quantitation of Neurotoxic Metabolites of the Kynurenine Pathway by Laser Desorption Ionization Mass Spectrometry (LDI-MS).

The metabolites of the mammalian kynurenine (KYN) pathway are generated from a branch of tryptophan metabolic pathway. The latter generates 3-hydroxykynurenine (3-HK), kynurenic acid (KYNA), quinolinic acid (QUIN), and picolinic acid (PIC) which are all strongly neuroactive, often with dramatically contrasting functional outcomes. Whereas KYNA and PIC are neuroprotective, 3-HK and QUIN are potently neurotoxic and attributed in major neurodegenerative diseases like schizophrenia, Alzheimer's disease, Huntington's disease, bipolar disorder, and depression. It is increasingly evident that the ratio(s) between the neurotoxic and neuroprotective metabolites may help predict the manifestations of disease vs. health. Therefore high-throughput platforms for determining the relative levels of these kynurenine metabolites in biofluids offer considerable potential. Current analytical tools for studying KYN pathway include assays of branching enzymes, PCR, immunoanalysis, and LCMS. None of these offer high-throughput, cost-effective analyses suited for clinical or drug-screening applications. In this report a laser desorption ionization mass spectrometry (LDI-MS) method is described using SBA-15 mesoporous silica. The system allows fast, high-resolution quantitation of neurotoxic kynurenines using targeted metabolomics on conventional MALDI platforms.

Enhanced permeation, leaf retention, and plant protease inhibitor activity with bicontinuous microemulsions.

Bicontinuous microemulsions (BCMEs) have excellent solubulizing properties along with low interfacial tension and aqueous content that can be controlled. In this work, water soluble plant protease inhibitor (PI), well characterized for its activity against insect pests, was incorporated into a BCME system and explored for permeation on hydrophobic leaf surfaces and protease inhibition activity. The bicontinuous nature of the microemulsion containing water:2-propanol:1-butanol (55:35:10 w/w) was characterized using conductivity and self-diffusion coefficient measurements. The PI was soluble in the water-rich bicontinuous domains, stable in the microemulsions, and protease inhibition activity was retained for a prolonged duration. The microemulsions ensured greater wettability and a wider spread of the PI on hydrophobic leaf surfaces as revealed by contact angle measurements. Significantly, trypsin inhibition activity assays of the PI recovered from the leaves after delivery from the microemulsion indicated a significant increase in the PI retention on the leaf. This BCME enabled greater leaf permeation and retention of the PI can be attributed to a temporary disruption of the waxy leaf surface followed by self-repair without causing any long term damage to the plant.