Understanding what the concentration of a compound in the body is and how the concentration varies with time (pharmacokinetics) is an essential foundation of modern drug discovery and development. Taking this further and developing an understanding of how these concentrations are driving in vivo effects (biomarkers/efficacy/toxicity) and the time that these concentrations need to be maintained for a positive outcome starts to allow rational decisions to be made about the dose and pharmacokinetics (PK) profile required in patients. This allows a drug developer to answer questions such as:
As a compound progresses into safety testing and especially clinical development there are strong regulatory, and publication needs to describe the pharmacokinetics of a compound. This is initially via non-compartmental analysis (NCA) which is used to establish descriptive statics of the compound’s human pharmacokinetics and provide insight on its performance (for instance does exposure increase proportionally with dose and are the pharmacokinetics time independent?). Focussed absorption modelling can build understanding of the properties that effect drug exposure and inform formulation strategies. Alongside NCA characterisation of PK, translational PK-PD (pharmacodynamics) modelling can be performed to place emerging clinical data into context with preclinical data, understand potential sources of variability and uncertainty, and guide clinical investment decisions.
As clinical development progresses Population PK and Population PK-PD modelling becomes increasingly important to:
This analysis will be a key part of the argument justifying the choice of Phase 3 and Commercial dose to Regulatory Authorities while also allowing sponsors to understand if different doses are required for different sub-populations.
In later development an area of modelling that is becoming more important is physiologically based pharmacokinetic (PBPK) analyses for biopharmaceutics. Regulators, especially USA FDA, are prompting PBPK as a tool to focus on drug product quality attributes and a mechanistic understanding of their interaction with physiology to affect in vivo drug performance resulting in the definition of critical attributes of the drug product, manufacturing process and input materials. The application of PBPK analyses for biopharmaceutics is considered an important tool to support the more frequent development of clinically relevant drug product specifications
|PBPK and allometric scaling|
Seda’s team of experts has many years of experience of all forms of pharmacokinetic modelling across large pharma and biotech and across late discovery to launch. Seda’s focus is on modelling in real time (rather than a retrospective justification) to aid clear, objective, and decisive decision making. Our PK-PD modellers are particularly experienced in the Oncology Therapeutic area. The unique combination of biopharmaceutics, clinical pharmacology and dissolution expertise that resides in Seda alongside our modellers means we are well placed to support PBPK analyses for biopharmaceutics.
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Paul has held several senior science leadership roles in Academia and Large Pharma. These roles focused on applying the best science in projects to ensure optimal product performance in the patient, thus bridging pharmaceutical and clinical disciplines.
Immediately prior to founding Seda Pharmaceutical Development Services Paul was Senior Clinical Pharmacology Scientist at AstraZeneca and led the clinical pharmacology program to NDA/MAA for AZD9291/osimertinib, which had been awarded ‘breakthrough therapy’ status by the FDA. Formerly Paul was a Principal Scientist within Pharmaceutical Development (AZ) leading both the global biopharmaceutics network and the Medicines Evaluation science community (USA, SE, UK).
Paul has been at the forefront of recent advances in the understanding of in vitro performance criteria that assure product performance in the patient including clinically relevant dissolution specifications (CRDS). Paul has co-authored papers with FDA staff focussed on efforts to combine biopharmaceutics with ICH Q8 (BioRAM) and is past Chair of the AAPS ‘QbD and product performance’ focus group.
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A physics graduate, with over 3 years’ experience in the pharmaceutical industry. Jake has applied analytical, mathematical, and problem-solving skills to the assessment of the likelihood of drug candidate success and to identify the likely risks involved in the formulation of a drug. Areas of expertise include PK / PK-PD-TGI modelling in preclinical species and translation to man to aid in dose prediction. Jake has developed models and applications including Nora Max, an in silico absorption model, that aids decision making in late preclinical and early clinical drug development phases of drug development and enables the identification of the formulation technologies required for FiH.
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Claire Patterson is Senior Principal Scientist at Seda with a Master of Pharmacy (MPharm) and a Ph.D. in Pharmaceutics from the Universities of Nottingham and University College London respectively. Claire is an experienced Biopharmaceutics scientist having spent 12 years with former employer AstraZeneca with roles in Early and Late Stage Product Development, linking in vitro to in vivo product performance. Current focus areas include subcutaneous and complex parenteral biopharmaceutics (including nanomedicines) and other non-oral routes of administration.
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Wang Wang Lee is a GPhC registered pharmacist with a PhD in Pharmaceutical Sciences and MBA from the University of Strathclyde. She has deep industry and academic experience particularly in the areas of biopharmaceutics and product development. Her expertise in early and late stage development, risk assessing and mitigating the development challenges has helped clients transition compounds into the clinic from discovery. Most recently, she is interested in DMPK and clinical pharmacology projects for poorly solubles and complex parenterals.
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Harri is able to apply commercially available software PK-Sim, in order to form translational PB-PK modelling for different species or populations and help with dose selection in the early clinical stage settings. As a Mathematics graduate his background enables him to bring systems thinking to PK modelling to identify sources of uncertainty and risk.