Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

Abstract

Introduction: CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time-and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentrationtime profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans. ARTICLE HISTORY

ABC Transporters at the Blood–Brain Interfaces, Their Study Models, and Drug Delivery Implications in Gliomas

“…Although it still needed to perform some experiments to feed the models or to confirm their results, this is considerably diminished, reducing this time and money-consuming step of drug development to fewer molecules with better pharmacokinetic and pharmacodynamic characteristics. In addition, there is a continuous work in developing more accurate and faster algorithms as tools for drug screening and many exist today, as has been reviewed previously [4,6,285]; thus, some of the most important will be explained below.…”

Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics

“…The transport over this barrier may be passive (driven by concentration gradient) and active (driven by transporters). In addition to the BBB, other factors, such as plasma protein binding, brain tissue binding, cellular uptake, brain metabolism, CSF flow, and physicochemical properties of the drug influence the drug exposure profile in the brain (for reviews and key research on this topic see references [36][37][38][39][40][41]). Although classical PK modeling still often is used, physiology-based PK (PBPK) modeling is increasingly applied to predict the time course of drug concentrations at the site of drug action.…”

Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D2 receptor activation using multi‐biomarker pharmacokinetic/pharmacodynamic modelling

“…Besides insufficient information on drug distribution into and within the brain, a main cause of attrition in CNS drug development is the lack of translational pharmacodynamic biomarkers, that is, preclinical biomarkers that are predictive for clinical effect (Hurko and Ryan, 2005;Soares, 2010;de Lange et al, 2017). This enables the mechanistic extrapolation of drug effects from animals to humans (Danhof et al, 2008;de Lange et al, 2017).…”