What is Pharmacometrics?
How to find the right drug and the right dosing regimen for a patient? How to make use of the wealth of information available - about the drug, the patho-/physiology, in vitro, nonclinical and clinical data? Pharmacometrics is the integrative science using in silico models to understand the drug-patient-disease relationships and to optimise drug therapy.
The science of pharmacometrics ...
... aims at understanding the drug-patient-disease interaction by analysing, e.g., drug concentration, effect and disease progression data over time.
... develops and uses mathematical & statistical models utilising in silico, in vitro and in vivo preclinical and clinical data.
The ultimate goal is to contribute to decision-making for better drug therapies in patients and to foster rational use of medicines in individuals.
To describe the science of pharmacometrics more specifically, continue to read depending on your background.
I have a background in pharmacy/pharmaceutical sciences/life sciences
Knowledge gaps. In drug development and drug therapy, the complex interaction of the drug and the patient with the disease is rarely fully understood. As a result, patients may not (fully) benefit from their therapy, or suffer from (serious) adverse drug reactions.
Science of pharmacometrics. The science of pharmacometrics aims to generate new knowledge of the underlying mechanisms and processes of the drug-patient-disease interaction by exploring drug, patient/organism, disease data over time and developing thereon so called pharmacometric models.
Pharmacometric models and usage. These models represent the functional relationships in the chain of: administered dosing regimen — resulting drug concentration-time profiles — effect-time profiles — therapeutic outcome (success or toxicity). Pharmacometric models can then be used to make predictions for new scenarios in silico (i.e. computer-based) such as alternative dosing regimens (e.g. once- vs twice-daily; dose in-/decrease) or different patient populations (e.g. in paediatric, kidney failure or critically ill patients). Without having to perform a new clinical trial, these prdictions allow to study and identify, e.g.,
- whether effective and safe concentrations are being reached
- the extent and duration of desired and undesired effects
- whether the therapeutic outcome will be cure or symptom relief, decelerating disease progression, therapeutic failure
- the severity and duration of adverse drug reactions/toxicity or the development of drug resistance.
These predictions can be performed both on the individual patient level or subpopulation level (e.g. patients with impaired renal function).
Methodological approach. Pharmacometric approaches integrate data and knowledge from various sources, such as clinical trials, observational studies, in vitro or ex vivo or nonclinical investigations and literature in a coherent mathematical framework to represent the relevant processes in the body. Future advancements in pharmacometric modelling and simulation will include even more and diverse data, and will more and more capture the relevant physiological and pathophysiological pathway and network features of the body.
Goal and impact. To this end, pharmacometrics is internationally increasingly applied in ‘Model-informed drug discovery and development’ (MID3) and in ‘Model-informed patient care’ (MIPC) concepts—in academia, research-driven pharmaceutical industries, regulatory and evaluating authorities—with the ultimate goal to contribute to rational decision-making to optimise drug therapies for individual patients and to foster rational use of medicines in patients.
I have a background in mathematics/statistics or bioinformatics/systems biology
The challenges. In drug development and current drug therapy, the amount of data generated and the complexity of the processes studied is continuously growing, generating the need for advanced mathematical/statistical approaches to analyse, understand and interpret such data. Systems biological approaches are employed to develop adequate models of pharmacologically relevant processes. Computational methods are used to quantitatively and qualitatively study and characterise the complex interaction and relationship between the drug, patient and disease. As a consequence, applied mathematics, statistics as well as bioinformatics and systems biology are key disciplines of pharmacometric research.
Many important open questions. What are the relevant processes that determine the absorption, distribution and elimination of a drug after administration? Does the drug reach its target? What is the best drug-concentration profile to optimally interfere with the targeted systems? Does the targeted system recover and/or develop resistance to the drug? How to integrate data into the modelling process to update our knowledge? How to reduce complex networks to its relevant essential feature? How to integrate complex mechanistic models and statistical approaches in the presence of sparse data? How to efficiently estimate parameters in hierarchical statistical models? How to optimally design future experiments, given our current knowledge?
Overall aim. The science of pharmacometrics aims to raise our understanding of the relevant pharmaceutical processes to a level that allows us to make confident and sufficiently precise predictions. To this end, theoretically justified and efficient mathematical/ statistical approaches are of key importance—in combination with sound pharmacological and patho-/physiological knowledge, models and experimental data.
The impact. In the form of model-informed drug discovery and development, pharmacometric approaches have been implemented in research-driven pharmaceutical companies as well as regulatory agencies (that decide on the approval of new medicines). In silico, i.e., computer-based, predictions are already used to determine the required dose adjustments in case of potential drug-drug interactions or within special populations (e.g., with reduced activity of the liver or the kidneys). Such results have already been incorporated in the label (package insert) of approved medicines.