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. 2022 Mar 7;39(7):1363–1392. doi: 10.1007/s11095-022-03193-2

Table VI.

Challenges and Prospects in the Development of Transporter-Utilizing (Pro)Drugs

In Vitro

• Exploration of expression and function of brain-selective enzymes to achieve site-selective bioconversion of prodrugs

• Applying time-dependent experiments accompanied by computational methods to separate transported substrates from binding ligands

• Evaluation of intracellular pharmacoproteomics to optimize the efficacy of transporter-utilizing compounds

• Optimizing the affinity and the interactions of the substrates with adequate in vitro and computational methods (inducing dynamic process) to attain compounds that can compete with endogenous substrates for transporter utilization

In Vivo

• Utilization of quantitative proteomic data together with pharmacokinetic studies (pharmacoproteomics) to understand the drug disposition between the CNS and periphery

• Characterization of transporter expression in the selected diseases during the early phase of the drug development phase to understand if there are changes in pharmacoproteomics as a part of the pathology

• Discovering novel biomarkers related to transporter function to enable monitoring the disease conditions, progress, and effects of drug therapy

• Exploring epigenetic regulation of the transcriptional and post-transcriptional mechanisms of drug transporters to predict the response of the CNS-therapies and attaining the personalized medicine

• Studying and correlating the brain permeation data correctly from nocturnal rodents to diurnal humans to understand the effects of circadian rhythms at the CNS barriers

In Silico

• Understanding dynamic processes of protein by utilizing advanced computational methods, such as MDS, instead of using static protein models for protein-ligand interactions

• Screening compounds towards several transporters and using machine learning for the prediction of overlapping substrate specificities and possible interactions with efflux transporter

• Utilization of deep learning and generative methods in chemoinformatics and chemical biology in structural design and develop brain-targeted transporter-utilizing compounds with desired properties