The workshop is neither trying to provide a 101 course on LC-MS proteomics nor attempts to focus on what is new in LC-MS proteomics techniques. Attendees need to have some background but not necessarily leading experts in the field. The consensus report from the previous ISSX Workshop held in 2018 (Prasad et al. 2019, Clinical Pharmacology and Therapeutics) is considered a background reading. The recent Symposium Repot from North American ISSX 2023 in Boston (Prasad et al 2024, Drug Metabolism Disposition, In Press) captures the content of lectures and workshop exercises. Hence, the event builds on what is known in the literature in relation to LC-MS proteomics and provides more practical guidance for those who are expanding their research activities in this area, such that they do not go through pitfalls and get to the end point faster.

The human genome comprises approximately 20,000 protein-coding genes and over 900 million variants according to dbSNP. Systematic understanding of the impact of genomic alterations in humans is critical for the development of effective medicines. However, it is simply not feasible to study every single variant in detail. This challenge extends to the analysis of how pharmacogenes are affected by genetic polymorphisms, as it is impossible to study the impact of every individual single nucleotide polymorphisms/variations (SNPs/SNVs) of pharmacogenes in human clinical trials. Yet, understanding drug metabolism and pharmacokinetics is crucial for assessing drug efficacy and safety. To minimize harmful side effects from drugs while maximizing their therapeutic effectiveness in each patient or group of patients, we would need to understand the effects of population specific SNPs in pharmacogenes and drug-enzyme interactions. To date the effect of non-synonymous SNPs, more specifically missense mutations, at the protein level is poorly studied in pharmacogenomics research. We previously proposed a post-hoc analysis approach of molecular dynamics (MD) simulations using dynamic residue network (DRN) analysis to consider the dynamic nature of functional proteins and protein-drug complexes and to probe the impact of mutations and their allosteric effects. This talk will discuss the computational approaches and tools that we have developed over the years with applications to pharmacogenomics.

This symposium/webinar is intended to educate ADME scientists on MPS technology and its potential to generate clinically translatable data.

In this webinar we will discuss efforts for improving the global underrepresentation of African genetics in science. We will further explore the current landscape and challenges in realizing an African hepatic modeling platform - with a specific focus on the development of hiPSC models that can better recapitulate hepatic function.

The human genome comprises approximately 20,000 protein-coding genes and over 900 million variants according to dbSNP. Systematic understanding of the impact of genomic alterations in humans is critical for the development of effective medicines. However, it is simply not feasible to study every single variant in detail. This challenge extends to the analysis of how pharmacogenes are affected by genetic polymorphisms, as it is impossible to study the impact of every individual single nucleotide polymorphisms/variations (SNPs/SNVs) of pharmacogenes in human clinical trials. Yet, understanding drug metabolism and pharmacokinetics is crucial for assessing drug efficacy and safety. To minimize harmful side effects from drugs while maximizing their therapeutic effectiveness in each patient or group of patients, we would need to understand the effects of population specific SNPs in pharmacogenes and drug-enzyme interactions. To date the effect of non-synonymous SNPs, more specifically missense mutations, at the protein level is poorly studied in pharmacogenomics research. We previously proposed a post-hoc analysis approach of molecular dynamics (MD) simulations using dynamic residue network (DRN) analysis to consider the dynamic nature of functional proteins and protein-drug complexes and to probe the impact of mutations and their allosteric effects. This talk will discuss the computational approaches and tools that we have developed over the years with applications to pharmacogenomics.

Physiology-based pharmacokinetic (PBPK) models are broadly applied in late phase drug discovery/ development. Traditionally, compounds are prioritized based on hierarchical filtering with predefined cut-offs for desirable range of various parameters. PBPK models, when combined with related pharmacodynamic assumptions, offer a valuable platform to integrate multiple parameters driving the in vivo PK profile required for target engagement. They also provide mechanistic interpretation of key drivers for the predicted profile to further help with compound design strategies. As such, these results enable compound prioritization in a holistic manner, focusing on multi-property optimization (MPO). This presentation will provide a brief overview of the structure and application of an internal PBPK model. Examples of successful application of this tool on a small molecule drug discovery program will be shared to illustrate its role in driving decisions to guide compound progression.

The purpose of this webinar is to: 1. Provide valuable knowledge exchange between ADC developers andthe Regulatory Agency (FDA) 2. Hear from leading experts in PK/PD, pharmacometrics, & clinical pharmacology, plus clinicians and regulatory minds, fostering collaborations and partnerships that can propel ADC projects seamlessly. 2. Demonstrate the successful integration of pharmacokinetics and pharmacometrics in ADC clinical development, enhancing your ability to apply these principles to your own work

Pharmacogenomics is relevant worldwide for modern therapeutics and yet needs further uptake in developing countries. There is paucity of studies with a naturalistic design in real-life clinical practice in patients with comorbidities and multiple drug treatments. To evaluate the role and impact of host underlying genetics on treatment response, different approaches are used depending on existing levels of understanding on the functional significance of genetic variants in question. This lecture showcases the work done pharmacogenomics research and how this has occurred in Africa and how “omics” is being leveraged. Our approach focuses on common disease conditions and commonly used medications including herbal medicinal plants. We report on antiretroviral therapy and other treatments alter microRNA expression signatures and expression of drug-metabolizing enzyme genes, in vitro. These data point to several important clinical implications through changes in drug/drug interaction risks and achieving optimal therapeutics. Thus, differential expression of microRNAs after treatment with EFV and RMP adds another layer of complexity that should be incorporated in pharmacogenomic algorithms to render drug response more predictable. The lecture will reflect also on pharmacogenomics of herbal medicines, and interaction with conventional drugs. There is a trend of important genes and their variants coming being prominent biomarkers for responses for commonly used drugs. The use of a wholistic approach in pharmacogenomics research translation that transcends disciplinary boundaries incorporating different “omics” ultimately leading to precision medicine.

This webinar is intended to provide education and updates of this evolving area to scientists and graduate students.