Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model

Kosugi, Yukio; Mizuno, Kunihiko; Santos, Cipriano; Sato, Sho; Hosea, Natalie; Zientek, Michael

doi:10.1208/s12248-021-00604-x

Cited by 8 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in silico models for f u,brain were constructed using SVMs by Wan et al [92] and Dolgikh et al [90], both of which demonstrated a relatively high predictive performance with R 2 vales of 0.782 and 0.64, respectively. Interestingly, Kosugi et al [93] demonstrated that ML models combined with both in vitro P-gp and BCRP efflux ratios (ERs) yielded predicted K p,uu,brain values that correlated well with in vivo data when compared to ML models.…”

Section: Unbound Brain-to-plasma Partition Coefficient and Brain Homo...mentioning

confidence: 96%

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Komura,

Watanabe,

Mizuguchi

2023

Pharmaceutics

View full text Add to dashboard Cite

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

show abstract

Section: Unbound Brain-to-plasma Partition Coefficient and Brain Homo...mentioning

confidence: 96%

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Komura,

Watanabe,

Mizuguchi

2023

Pharmaceutics

View full text Add to dashboard Cite

show abstract

“…The BEE score is implemented in MOE software as an SVL utility to predict Kp,uu, and Cu,b (unbound concentration of the molecule in the brain). More recently, Kosugi et al [ 105 ] reported improvement in the predictivity and coverage of application by machine learning approaches for Kp,uu prediction by incorporating in vitro P-gp and BCRP activities.…”

Section: Active Transport Across the Bbb (Efflux Transporters Influx ...mentioning

confidence: 99%

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

Gupta,

Feng,

Bhisetti

2024

Molecules

View full text Add to dashboard Cite

In CNS drug discovery, the estimation of brain exposure to lead compounds is critical for their optimization. Compounds need to cross the blood–brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro, and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand unbound brain exposure by deriving the Kp,uu parameter and the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. The MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores, and various machine learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds, building of a CNS penetrable compounds library, and optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand the reliability and ability of these methods to predict CNS penetration. We review the in silico, in vitro, and in vivo data and their correlation with each other, as well as assess published experimental and computational approaches to predict the BBB penetrability of compounds.

show abstract

“…Recent mathematical model efforts include incorporation of P-gp efflux ratio 56,57 and BCRP. 58 The Brain Exposure Efficiency Score is another model approach for including influx and efflux active transporters. 59 CNS physiologically-based PK (PBPK) models are more complex by taking into account many PK processes.…”

Section: In Silico Modelsmentioning

confidence: 99%

“…This is because incorporation of active transport at the BBB needs additional attention. Recent mathematical model efforts include incorporation of P‐gp efflux ratio 56,57 and BCRP 58 . The Brain Exposure Efficiency Score is another model approach for including influx and efflux active transporters 59 …”

Section: Assessment Of Bbb Transport and Intra‐brain Distributionmentioning

confidence: 99%

Understanding the Blood‐Brain Barrier and Beyond: Challenges and Opportunities for Novel CNS Therapeutics

Lange

Udenaes

2022

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

This review addresses questions on how to accomplish successful central nervous system (CNS) drug delivery (i.e., having the right concentration at the right CNS site, at the right time), by understanding the rate and extent of blood‐brain barrier (BBB) transport and intra‐CNS distribution in relation to CNS target site(s) exposure. To this end, we need to obtain and integrate quantitative and connected data on BBB using the Combinatory Mapping Approach that includes in vivo and ex vivo animal measurements, and the physiologically based comprehensive LEICNSPK3.0 mathematical model that can translate from animals to humans. For small molecules, slow diffusional BBB transport and active influx and efflux BBB transport determine the differences between plasma and CNS pharmacokinetics. Obviously, active efflux is important for limiting CNS drug delivery. Furthermore, liposomal formulations of small molecules may to a certain extent circumvent active influx and efflux at the BBB. Interestingly, for CNS pathologies, despite all reported disease associated BBB and CNS functional changes in animals and humans, integrative studies typically show a lack of changes on CNS drug delivery for the small molecules. In contrast, the understanding of the complex vesicle‐based BBB transport modes that are important for CNS delivery of large molecules is in progress, and their BBB transport seems to be significantly affected by CNS diseases. In conclusion, today, CNS drug delivery of small drugs can be well assessed and understood by integrative approaches, although there is still quite a long way to go to understand CNS drug delivery of large molecules.

show abstract

Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model

Cited by 8 publications

References 43 publications

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

Understanding the Blood‐Brain Barrier and Beyond: Challenges and Opportunities for Novel CNS Therapeutics

Contact Info

Product

Resources

About