All medicines have the capacity to yield benefit or to cause harm, and in the 16th Century, the alchemist Paracelsus observed that only “the dosage makes it either a poison or a remedy”. With few exceptions, today’s medicines are associated with significant inter-individual variability in plasma drug exposure despite standardised dosing, and in many cases this may be associated with variability in response to therapy.
Paracelsus also asserted that “Medicine ... does not consist of compounding pills and plasters; it deals with the very processes of life, which must be understood before they may be guided”. This is as true now as it was then: understanding the underlying biology of disease guides the question to be asked, the design of the study, and the interpretation of its findings. In the BAF, we uphold this belief by combining leading technological expertise in drug analysis, with academic and clinical expertise in the disease, and how it is managed. Historically the BAF undertook clinical studies only in HIV, TB and malaria but as our pool of research expertise has grown, we are expanding into other infections such as hepatitis C, and also into other non-infectious diseases where we have existing research strength.
We measure drug concentrations: We also believe that understanding the relationship between drug exposure and efficacy/toxicity (the ‘PK-PD relationship’) is fundamental in optimising its use. Conventionally, exposure is described in plasma. Ultimately it is the drug concentration at its target site which matters, so we aim also to measure drug concentrations at, or as close to the site of action as possible. Often this means analysing very small amounts of sample (eg tissue biopsy or cellular subsets), or else working with complex and challenging matrices (eg breast milk, genital secretions, rectal swabs). To achieve this, we are supported by a suite of high-end, triple quadrupole mass spectrometer systems (Thermo Fisheer, SCIEX).
We use mathematical modelling to interpret these data: Modelling (non-linear mixed-effects) provides a quantitative description of population variability, and likely factors associated with drug exposure. Simulations using established models can allow us to evaluate how drug exposure may alter under diverse scenarios (eg different dosing, or in children, pregnancy or with drug interactions, and pharmacogenetic impact).