Investigation of the role and quantitative impact of breast cancer resistance protein on drug distribution into brain and CSF in rats

Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models

Machine learning (ML) approaches have been applied to predicting drug pharmacokinetic properties. Previously, we predicted rat unbound brain-to-plasma ratio (Kpuu,brain) by ML models. In this study, we aimed to predict human Kpuu,brain through animal ML models. First, we re-evaluated ML models for rat Kpuu,brain prediction by using trendy open-source packages. We then developed ML models for monkey Kpuu,brain prediction. Leave-one-out cross validation was utilized to rationally build models using a relatively small dataset. After establishing the monkey and rat ML models, human Kpuu,brain prediction was achieved by implementing the animal models considering appropriate scaling methods. Mechanistic NeuroPK models for the identical monkey and human dataset were treated as the criteria for comparison. Results showed that rat Kpuu,brain predictivity was successfully replicated. The optimal ML model for monkey Kpuu,brain prediction was superior to the NeuroPK model, where accuracy within 2-fold error was 78% (R2 = 0.76). For human Kpuu,brain prediction, rat model using relative expression factor (REF), scaled transporter efflux ratios (ERs), and monkey model using in vitro ERs can provide comparable predictivity to the NeuroPK model, where accuracy within 2-fold error was 71% and 64% (R2 = 0.30 and 0.52), respectively. We demonstrated that ML models can deliver promising Kpuu,brain prediction with several advantages: (1) predict reasonable animal Kpuu,brain; (2) prospectively predict human Kpuu,brain from animal models; and (3) can skip expensive monkey studies for human prediction by using the rat model. As a result, ML models can be a powerful tool for drug Kpuu,brain prediction in the discovery stage.

In vitro identification of decreased function phenotype ABCG2 variants

Reduced activity of efflux transporter ABCG2, caused e.g., by inhibition or decreased function genetic variants, can increase drug absorption and plasma levels. ABCG2 has one clinically significant single nucleotide variant Q141K (c.421C>A), which leads to decreased protein levels and transport activity. In addition to Q141K, ABCG2 has over 500 rare (<1% minor allele frequency) nonsynonymous variants, but their functionality remains unknown. We studied the transport activity and abundance of 30 rare ABCG2 variants. The variants were transiently expressed in HEK293 cells. Transport activity and protein abundance were measured from inside-out crude membrane vesicles. Results were normalised to the reference ABCG2, while Q141K was used to categorise variants into decreased and normal function phenotypes based on their apparent transport activity. Fourteen variants (G80E, D128V, T434M, Q437R, C438R, C438W, C438Y, L479S, P480L, S486N, T512N, S519P, G553D and K647E) had similar or lower apparent transport activity than Q141K and thus were categorised as having a decreased function phenotype. Protein abundance could not explain all of the observed changes in transport activity: Only six variants (D128V, Q437R, C438R, S519P, G553D, and K647E) had similar or lower abundance compared to Q141K. The decreased function variants may increase systemic drug exposure and therefore cause interindividual variability in pharmacokinetics. In the future, in vitro phenotype classification may help to design personalised drug treatments.

Recent advances in circadian-regulated pharmacokinetics and its implications for chronotherapy

Dependence of pharmacokinetics and drug effects (efficacy and toxicity) on dosing time has long been recognized. However, significant progress has only recently been made in our understanding of circadian rhythms and their regulation on drug pharmacokinetics, efficacy and toxicity. This review will cover the relevant literature and a series of publications from our work summarizing the effects of circadian rhythms on drug pharmacokinetics, and propose that the influence of circadian rhythms on pharmacokinetics are ultimately translated into therapeutic effects and side effects of drugs. Evidence suggests that daily rhythmicity in expression of drug-metabolizing enzymes and transporters necessary for drug ADME (absorption, distribution, metabolism and excretion) are key factors determining circadian pharmacokinetics. Newly discovered mechanisms for circadian control of the enzymes and transporters are covered. We also discuss how the rhythms of drug-processing proteins are translated into circadian pharmacokinetics and drug chronoefficacy/chronotoxicity, which has direct implications for chronotherapy. More importantly, we will present perspectives on the challenges that are still needed for a breakthrough in translational research. In addition, knowledge of the circadian influence on drug disposition has provided new possibilities for novel pharmacological strategies. Careful application of pharmacokinetics-based chronotherapy strategies can improve efficacy and reduce toxicity. Circadian rhythm-mediated metabolic and transport strategies can also be implemented to design drugs.