How mathematical modeling tools are helping the pharmaceutical sciences

The full drug pipeline to bring a new medication to the market (from the lab bench to the bedside) involves four main steps. First of all, a molecule has to be found able to demonstrate an activity against a molecular target involved in a disease; second, the active pharmaceutical ingredient (API) has to be properly formulated, with suitable excipients, in order to give the desired drug release kinetics; third, the medication has to be tested in-vivo (clinical trials) to verify its safety and its efficacy; last but not least, the medication which successfully survives at the end of clinical trials has to be produced on an industrial scale, without any loss of efficacy. Obviously its production and its marketing have to guarantee enough economic returns to cover the expenses of all these phases. Each one of these phases can be carried out on the basis of the researchers’ experiences, according to trial-and-error processes or brute-force approaches (e.g. in the first step, each candidate molecule can be used against a target in an animal model, in order to find a drug potentially useful in human beings, as in classical pharmacology—also known as forward pharmacology)