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

Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction

Background Numerous drugs have the potential to be affected by cytochrome P450 (CYP) 2B6-mediated drug-drug interactions (DDIs). Objectives In this work, we extend a static approach to the prediction of the extent of pharmacokinetics DDIs between substrates and inhibitors or inducers of CYP2B6. Methods This approach is based on the calculation of two parameters (the contribution ratio [CR], representing the fraction of dose of the substrate metabolized via this pathway and the inhibitory or inducing potency of the perpetrator [IR or IC, respectively]) calculated from the area under the concentration-time curve (AUC) ratios obtained in in-vivo DDI studies. Results Forty-eight studies involving 5 substrates, 11 inhibitors and 18 inducers of CYP2B6 (overall 15 inhibition and 33 induction studies) were divided into test and validation sets and considered for estimation of the parameters. The proposed approach demonstrated a fair accuracy for predicting the extent of DDI related to CYP2B6 inhibition and induction, all predictions related to the validation test (N = 18) being 50-200% of the observed ratios. Conclusions This methodology can be used for proposing initial dose adaptations to be adopted, for example in clinical use or for designing DDI studies involving this enzyme

Bayesian population approaches to the analysis of dose escalation studies

In dose escalation studies cohorts of subjects are given increasing doses of a candidate drug to assess safety and tolerability, pharmacokinetics and pharmacological response. The escalation is carried on until a predefined stopping limit is achieved, often identified by a pharmacokinetic endpoint such as peak plasma concentration or area under the plasma concentration-time profile. In the present work, the application of Bayesian methodologies to Phase I dose escalation studies is explored. A Bayesian population model is devised, which provides predictions of dose-response and dose-risk curves, both for individuals already enrolled in the trial and for a new, previously untested subject. Empirical and fully Bayesian estimation algorithms are worked out. Such methods provide equivalent performances on both experimental and simulated datasets. With respect to previous work, it is quantitatively proven not only that a more general and flexible model is identifiable, but also that such flexibility is needed in real scenarios

Physiologically based pharmacokinetics (PBPK)

Allometric scaling is widely used to predict human pharmacokinetic parameters from preclinical species, and many different approaches have been proposed over the years to improve its predictive performance. Nevertheless, prediction errors are commonly observed in the practical application of simple allometry, for example, in cases where the hepatic metabolic clearance is mainly determined by enzyme activities, which do not scale allometrically across species. Therefore, if good correlation was noted for some drugs, poor correlation was observed for others, highlighting the need for other conceptual approaches. Physiologically based pharmacokinetic (PBPK) models are now a well-established approach to conduct extrapolations across species and to generate simulations of pharmacokinetic profiles under various physiological conditions. While conventional pharmacokinetic models are defined by drug-related data themselves, PBPK models have richer information content and integrate information from various sources, including drug-dependent, physiological, and biological parameters as they vary in between species, subjects, or with age and disease state. Therefore, the biological and mechanistic bases of PBPK models allow the extrapolation of the kinetic behavior of drugs with regard to dose, route, and species. In addition, by providing a link between tissue concentrations and toxicological or pharmacological effects, PBPK modeling represents a framework for mechanistic pharmacokinetic-pharmacodynamic models

Pharmacokinetics in special populations

Pharmacokinetics are typically dependent on a variety of physiological variables (e.g., age, ethnicity, or pregnancy) or pathological conditions (e.g., renal and hepatic insufficiency, cardiac dysfunction, obesity, etc.). The influence of some of these conditions has not always been thoroughly assessed in the clinical studies of antiallergic drugs. However, the knowledge of the physiological grounds of the pharmacokinetics can provide some insight for predicting the potential alterations and guiding the initial prescription strategies. It is important to recognize that both pharmacokinetic and pharmacodynamic differences between populations should be considered. The available information on drugs used for the therapy of allergic diseases is reviewed in this chapter