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At the bench: the key role of PK–PD modelling in enabling the early discovery of biologic therapies
Author(s) -
Penney Mark,
Agoram Balaji
Publication year - 2014
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.12225
Subject(s) - drug development , drug discovery , computer science , risk analysis (engineering) , dosing , selection (genetic algorithm) , drug , pharmacokinetics , computational biology , clinical trial , medicine , pharmacology , biochemical engineering , bioinformatics , machine learning , engineering , biology
Pharmacokinetic–pharmacodynamic ( PK–PD ) modelling is already used extensively in pre‐clinical and clinical drug development to characterize drug candidates quantitatively, aid go/no‐go decisions and to inform future trial design and optimal dosing regimens. Less well known, although arguably as powerful, is its application at the earliest stages of drug development, at target selection and lead selection, where these same techniques can be used to predict and so bring forward drug candidates with the necessary characteristics or, for unachievable requirements, allow the abandonment of the programme for the minimum spend of time and cost. We consider three examples that illustrate the power of the application of modelling at this early stage. We start with the simple case of determining the optimal characteristics for a monoclonal antibody against a soluble ligand with its application to the investment decision for the development of best‐in‐class compounds. This is extended to the more complex situation of the target protein having an endogenous, inhibitory binding protein. We then illustrate how using physiologically‐based pharmacokinetic modelling enables the appropriate engineering and testing of biological therapeutics for optimal PK–PD characteristics. These examples illustrate how a minimal investment in modelling achieves orders of magnitude better returns in choosing the correct targets, mechanism of action and candidate characteristics to progress to clinical trials, streamlining drug development and delivering better medicines to patients.