Oligonucleotides have emerged as a rapidly growing class of therapeutics, with nearly 20 oligo-based drugs approved by the U.S. FDA to date. However, due to their novelty, complexity, and limited regulatory guidance, oligonucleotides pose unique technical challenges for drug developers compared to traditional small molecules or biologics. Specialized pharmacokinetic evaluation systems are essential to address these complexities.

Advancements in delivery platforms have heightened the need for customized PK strategies during preclinical development. For in vitro ADME studies, selecting the right metabolic model based on the drug's structure and in vitro-in vivo extrapolation (IVIVE) is critical. In vivo PK studies require tailored administration methods to thoroughly investigate pharmacokinetics. This webinar will discuss strategies for overcoming DMPK challenges and accelerating oligonucleotide drug development, drawing from extensive experience in early-stage screening and IND filings. 

CNS-targeting oligonucleotides add further complexity, primarily entering the brain through nervous system administration methods such as cerebrospinal fluid injections (ventricular, cisterna magna, and lumbar) and nasal administration. Among these, lumbar injection is preferred for its simplicity and feasibility for repeated dosing. Over years of refinement, a standardized platform for lumbar administration in monkeys has been developed.

This webinar is sponsored by WuXi AppTec.

As dictated by the free drug theory (FDT), fraction of unbound drug in plasma (fu,p) is routinely measured to rationalize pharmacological readouts such as drug potency and hepatic clearance. There is an increasing body of evidence contradicting the FDT when fu,p is applied, and one prominent example is the systematic under-prediction of hepatic clearance for highly bound compounds. We reason that fu,p is a static measure of drug binding extent and it does not capture drug protein binding dynamics. As a result, we have introduced the "dynamic free fraction" (fD) as a new binding parameter describing drug protein binding affinity/dynamics that can be indirectly determined by coupling the drug binding assay with a reporter enzyme in combination with high-resolution mass spectrometry, circumventing a long-standing challenge inherent in determining drug binding kinetics constants such as kon and koff. Using a large group of diverse drugs representing both CYPs and OATP transporter substrates, we demonstrated that the well-stirred model incorporating with fD correctly predicted both hepatic clearance and liver extraction ratio without apparent systematic bias, which is markedly better than those predicted with fu,p. The results suggest that dynamic free fraction (fD) as a measure of protein binding affinity is a key determinant in hepatic clearance, which is contrary to the currently held view.

Chronic kidney disease (CKD) is more than renal malfunction alone. It may affect drug disposition by multiple pathophysiological changes including the changes in abundancy and activity of hepatic drug transporters. Also, the disease factors may interplay with drug-drug interaction (DDI) in patients with CKD causing a different DDI scenario from the one in healthy volunteers. The translation of DDI magnitude between healthy subjects and patients with CKD may not be straightforward due to the complex drug-drug-disease interaction (DDDI) scenarios, whereas physiologically based pharmacokinetic (PBPK) model-based approach may serve as a valuable quantitative tool to predict the complex DDDI. Herein, we illustrate how to use PBPK modelling to predict the DDI between statins and roxadustat mediated by hepatic transporters in patients with severe CKD, and answer the following questions: how much does severe CKD affect the abundancy and activity of hepatic transporters? Which pathways(s) are the major one(s) responsible for the observed alteration of statins PK in patients with severe CKD? Is the magnitude of statin-roxadustat DDI in severe CKD patients similar to that in HV? What are the optimal dose regimens when statins and roxadustat are co-administered in patients with severe CKD?

This workshop is organized through the efforts of the ISSX Bioanalysis in ADME Science Focus Group and is organized by Drs. Matthew Albertolle, Bingming Chen, Joe Cannon, Mei Han, Thomas Kraft, John Tran, Jim Shen, Faye Vazvaei, and Brian Booth.

The scope of this workshop is to provide content for attendees from early career/grad school who want to learn fundamentals (Day 1) to senior career leaders in the field who want to learn about what is the new and emerging technologies (Day 2). Workshop topics can include bioanalytical techniques, including biotransformation (and subsequent bioanalysis) of macromolecules. Each session of the workshop consists of presentations from several expert speakers. In addition, there will be an interactive virtual poster session which will be selected from the pool of abstracts from attendees. 

The African continent harbors unparalleled genetic diversity yet remains largely underrepresented in  pharmaceutical  research  and  development.  Pharmacogenomics  is  fundamental  to  precision medicine strategies, and although to date largely absent from implementation, representing the genetic diversity of the African population within the laboratory is critical to the democratization of stratified  and  effective  healthcare  in  Africa.  Afrocentric  preclinical  resources  could  support precision  medicine on  the  continent  and  reshape  the  global  underrepresentation  of  African genetics  in  science. Models  which  could  begin  to  address  this  include  primary  human hepatocytes,  immortalized  hepatic  cell  lines,  and human induced  pluripotent  stem  cell  (hiPSC)derived hepatocyte-like cells – derived from individuals of African genetic ancestry. The feasibility of an African hepatic modeling platform is slowly being realized due to the convergence of several technologies and methodologies which have traditionally been siloed on the continent.

Despite billions in annual investment, most drugs never reach the market because preclinical experiments fail to predict human effects. In this webinar, we will cover why in vivo models only deliver an approximation of what is likely to happen in the human body and why building more predictive, human-relevant models is key to reducing drug attrition. During the webinar we will introduce ADME scientists to the field of microphysiological systems (MPS), also known as organ-on-a-chip (OOC).

We will explore how MPS technology can be implemented to create human-specific tissue models such as the gut and liver and interconnect them into systems that generate clinically translatable data. We will demonstrate how a deeper and earlier knowledge of human drug ADME profiles enables issues to be addressed prior to costly preclinical studies.

The webinar will conclude by highlighting how the complementary use of MPS bridges the human-relevance gap, facilitating the confident selection of safer and more effective drug candidates, whilst also reducing costs.

This webinar is sponsored by CN Bio Innovations.

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 optimal PK necessary for a drug candidate to achieve efficacy is highly dependent on the targeted PD, a relationship often not well characterized during the early stages of drug discovery. Relying on generic assumptions about PK and potency can misguide screening and compound design, leading to suboptimal ADME or molecular properties. This, in turn, may increase attrition rates and extend hit-to-lead and lead optimization timelines. The "PD before PK" approach, detailed in this presentation, can be applied in two ways: forward, to virtually screen compounds for potential effectiveness, and reverse, to identify pharmacology-specific PK drivers and the related potency-ADME space early in discovery. This strategy aims to enhance the probability of success and reduce clinical attrition. Potential applications of this approach will also be discussed.

Antibody-drug conjugates (ADCs) are distinct from both biologics and small molecule drugs. There are special clinical pharmacology considerations for ADC development and approval due to their unique structure and mechanism of action. The FDA has approved 11 ADCs as of Aug. 2024 and published the final guidance: Clinical Pharmacology Considerations for Antibody-Drug Conjugates in March 2024. Using approved ADCs as illustrative examples, this lecture will cover key clinical pharmacology considerations regarding ADC dosing strategies, intrinsic factor evaluation, and drug-drug interaction assessment. It will also cover other topics such as bioanalytical approach, dose/exposure-response analysis, QTc assessment, immunogenicity, and post-marketing requirements/commitments. Finally, examples for new generations of antibody conjugates will be discussed. This presentation will inform more efficient clinical pharmacology development strategies for ADCs and help bring more of these therapies to patients.

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.