Literature DB >> 23294027

Improving the prediction of drug disposition in the brain.

Kiril Lanevskij1, Pranas Japertas, Remigijus Didziapetris.   

Abstract

INTRODUCTION: Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task. AREAS COVERED: This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals. EXPERT OPINION: Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property.

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Year:  2013        PMID: 23294027     DOI: 10.1517/17425255.2013.754423

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  9 in total

Review 1.  Toward precision medicine in glioblastoma: the promise and the challenges.

Authors:  Michael D Prados; Sara A Byron; Nhan L Tran; Joanna J Phillips; Annette M Molinaro; Keith L Ligon; Patrick Y Wen; John G Kuhn; Ingo K Mellinghoff; John F de Groot; Howard Colman; Timothy F Cloughesy; Susan M Chang; Timothy C Ryken; Waibhav D Tembe; Jeffrey A Kiefer; Michael E Berens; David W Craig; John D Carpten; Jeffrey M Trent
Journal:  Neuro Oncol       Date:  2015-05-01       Impact factor: 12.300

2.  Prediction of blood-brain barrier permeability of organic compounds.

Authors:  A S Dyabina; E V Radchenko; V A Palyulin; N S Zefirov
Journal:  Dokl Biochem Biophys       Date:  2016-11-06       Impact factor: 0.788

3.  Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

Authors:  Yaxia Yuan; Fang Zheng; Chang-Guo Zhan
Journal:  AAPS J       Date:  2018-03-21       Impact factor: 4.009

Review 4.  Molecular determinants of blood-brain barrier permeation.

Authors:  Werner J Geldenhuys; Afroz S Mohammad; Chris E Adkins; Paul R Lockman
Journal:  Ther Deliv       Date:  2015-08-25

5.  Crocetin promotes clearance of amyloid-β by inducing autophagy via the STK11/LKB1-mediated AMPK pathway.

Authors:  Abubakar Wani; Sweilem B Al Rihani; Ankita Sharma; Brenna Weadick; Rajgopal Govindarajan; Sameer U Khan; Parduman R Sharma; Ashish Dogra; Utpal Nandi; Chilakala N Reddy; Sonali S Bharate; Gurdarshan Singh; Sandip B Bharate; Ram A Vishwakarma; Amal Kaddoumi; Ajay Kumar
Journal:  Autophagy       Date:  2021-01-19       Impact factor: 16.016

Review 6.  Developments in Blood-Brain Barrier Penetrance and Drug Repurposing for Improved Treatment of Glioblastoma.

Authors:  Bryan G Harder; Mylan R Blomquist; Junwen Wang; Anthony J Kim; Graeme F Woodworth; Jeffrey A Winkles; Joseph C Loftus; Nhan L Tran
Journal:  Front Oncol       Date:  2018-10-23       Impact factor: 6.244

7.  Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.

Authors:  Eugene V Radchenko; Alina S Dyabina; Vladimir A Palyulin
Journal:  Molecules       Date:  2020-12-13       Impact factor: 4.411

8.  Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power.

Authors:  Tzu-Hui Yu; Bo-Han Su; Leo Chander Battalora; Sin Liu; Yufeng Jane Tseng
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

9.  A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.

Authors:  Daqing Zhang; Jianfeng Xiao; Nannan Zhou; Mingyue Zheng; Xiaomin Luo; Hualiang Jiang; Kaixian Chen
Journal:  Biomed Res Int       Date:  2015-10-04       Impact factor: 3.411

  9 in total

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