Use of Physiology-Based Elements to Predict the Pharmacokineticsof New Drugs

In this thesis, the application of physiology-based pharmacokinetic (PBPK) concepts in early (e.g., preclinical to clinical interface) or late clinical development (prediction of drug-drug interaction and characterization of PK in special population) will be described, addressing some of the gaps that still are present. After an introductory chapter describing these gaps, the limited predictivity of PBPK for predicting the human PK was addressed. The basic whole-body PBPK approach used to predict the pharmacokinetics in humans was complemented with the parameter refinement derived by the use of the data obtained in the in vivo study in animals, using a Bayesian approach as implemented in the SAAM software, providing a significant improvement of the PK predictions in humans. Other aspects addressed by this thesis were the prediction of PK in subjects with liver and renal impairments. Despite the substantial improvement of the description of the physio-pathological changes linked to renal and hepatic impairment that are being included in the commercially available PBPK platform, the predictions of the PK in subjects with renal and hepatic impairment are still far from satisfactory, due to limitations in the description of the different presentation of the renal and liver disease, the lack of comprehensive functional tests, the limitations of the actual studies. PK data were therefore collected and analysed via different techniques to predict the ratio of the systemic exposure in subjects with impairment relative to that observed in healthy subjects. The smart use of multivariate analysis can also provide a substantial stimulus for a more detailed mechanistic understanding of the absorption and disposition changes to be expected in renal and liver disease. The predictions of DDIs can be considered one of the major successes in the application of the PBPK based modeling approaches. PBPK elements (the estimation of the clearance of a drug when it is co-administered with an inhibitor of cytochrome P-450 3A - one of the major drug metabolizing enzymes) were combined with a population PK approach (non-linear mixed effect models) to predict the potential level of drug-drug interaction. The exercise was motivated by the objective difficulties in designing clinical trials, due to the long terminal half-life (6-9 months) of the victim drug, bedaquiline. The applications described in this thesis demonstrated that numerous physiology-based approaches and considerations are available to facilitate the characterization and the utilization (and, in broad sense, the development) of new drugs. These approaches can be based on full whole-body physiology based pharmacokinetic models; however, simpler physiology-based elements can also be adopted. These physiology-based methodologies can be used in applications that spans the full range of the development of new drugs: from the pre-clinical lead identification/optimization to the late development/post-marketing phases. PBPK approaches can be efficiently combined with other modeling approaches. These combined “Quantitative Sciences” approaches can provide a more efficient handle to problems, increasing the understanding on how new drugs can be used and allowing to provide answers to a wide range of practical problems encountered during the drug research and development of new drugs. They can also provide additional stimuli for a more detailed mechanistic understanding at the basis of the translational aspects (discovery-preclinical-clinical interface; normal population-population with organ impairment-population with comedications) of the pharmacokinetics of new drugs. However, wider knowledge-base, better experiments and a more profound scientific understanding are still needed to improve the predictive assessments of PK in these translational settings.