Machine Learning Techniques Applied to the Study of Drug Transporters

Unraveling the mystery of citrate transporters in Alzheimer's disease: An updated review

A key molecule in cellular metabolism, citrate is essential for lipid biosynthesis, energy production, and epigenetic control. The etiology of Alzheimer's disease (AD), a progressive neurodegenerative illness marked by memory loss and cognitive decline, may be linked to dysregulated citrate transport, according to recent research. Citrate transporters, which help citrate flow both inside and outside of cells, are becoming more and more recognized as possible participants in the molecular processes underlying AD. Citrate synthase (CS), a key enzyme in the tricarboxylic acid (TCA) cycle, supports mitochondrial function and neurotransmitter synthesis, particularly acetylcholine (ACh), essential for cognition. Changes in CS activity affect citrate availability, influencing energy metabolism and neurotransmitter production. Choline, a precursor for ACh, is crucial for neuronal function. Lipid metabolism, oxidative stress reactions, and mitochondrial function can all be affected by aberrant citrate transport, and these changes are linked to dementia. Furthermore, the two main pathogenic characteristics of AD, tau hyperphosphorylation and amyloid-beta (Aβ) aggregation, may be impacted by disturbances in citrate homeostasis. The goal of this review is to clarify the complex function of citrate transporters in AD and provide insight into how they contribute to the development and course of the illness. We aim to provide an in-depth idea of which particular transporters are dysregulated in AD and clarify the functional implications of these dysregulated transporters in brain cells. To reduce neurodegenerative processes and restore metabolic equilibrium, we have also discussed the therapeutic potential of regulating citrate transport. Gaining insight into the relationship between citrate transporters and the pathogenesis of AD may help identify new indicators for early detection and creative targets for treatment. This study offers hope for more potent ways to fight this debilitating illness and is a crucial step in understanding the metabolic foundations of AD.

Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design

INTRODUCTION

Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs often leads to adverse drug reactions (ADRs) and therapeutic failure. As such, early prediction of such inhibitors is vital in drug development. In this context, the limitations of the traditional in vitro assays and QSAR models methods have been addressed by harnessing artificial intelligence (AI) techniques.

AREAS COVERED

This narrative review presents the insights gained from the application of AI for predicting DME and DT inhibitors over the past decade. Several case studies demonstrate successful AI applications in enzyme-transporter interaction prediction, and the authors discuss workflows for integrating these predictions into drug design and regulatory frameworks.

EXPERT OPINION

The application of AI in predicting DME and DT inhibitors has demonstrated significant potential toward enhancing drug safety and effectiveness. However, critical challenges involve the data quality, biases, and model transparency. The availability of diverse, high-quality datasets alongside the integration of pharmacokinetic and genomic data are essential. Lastly, the collaboration among computational scientists, pharmacologists, and regulatory bodies is pyramidal in tailoring AI tools for personalized medicine and safer drug development.

Precision medication based on the evaluation of drug metabolizing enzyme and transporter functions

Pharmacogenomics, therapeutic drug monitoring, and the assessments of hepatic and renal function have made significant contributions to the advancement of individualized medicine. However, their lack of direct correlation with protein abundance/non-genetic factors, target drug concentration, and drug metabolism/excretion significantly limits their application in precision drug therapy. The primary task of precision medicine is to accurately determine drug dosage, which depends on a precise assessment of the ability to handle drugs in vivo, and drug metabolizing enzymes and transporters are critical determinants of drug disposition in the body. Therefore, accurately evaluating the functions of these enzymes and transporters is key to assessing the capacity to handle drugs and predicting drug concentrations in target organs. Recent advancements in the evaluation of enzyme and transporter functions using exogenous probes and endogenous biomarkers show promise in advancing personalized medicine. This article aims to provide a comprehensive overview of the latest research on markers used for the functional evaluation of drug-metabolizing enzymes and transporters. It also explores the application of marker omics in systematically assessing their functions, thereby laying a foundation for advancing precision pharmacotherapy.

MONSTROUS: a web-based chemical-transporter interaction profiler

Transporters are membrane proteins that are critical for normal cellular function and mediate the transport of endogenous and exogenous chemicals. Chemical interactions with these transporters have the potential to affect the pharmacokinetic properties of drugs. Inhibition of transporters can cause adverse drug-drug interactions and toxicity, whereas if a drug is a substrate of a transporter, it could lead to reduced therapeutic effects. The importance of transporters in drug efficacy and toxicity has led regulatory agencies, such as the U.S. Food and Drug Administration and the European Medicines Agency, to recommend screening of new molecular entities for potential transporter interactions. To aid in the rapid screening and prioritization of drug candidates without transporter liability, we developed a publicly available, web-based transporter profiler, MOlecular traNSporT inhibitoR and substrate predictOr Utility Server (MONSTROUS), that predicts the potential of a chemical to interact with transporters recommended for testing by regulatory agencies. We utilized publicly available data and developed machine learning or similarity-based classification models to predict inhibitors and substrates for 12 transporters. We used graph convolutional neural networks (GCNNs) to develop predictive models for transporters with sufficient bioactivity data, and we implemented two-dimensional similarity-based approach for those without sufficient data. The GCNN inhibitor models have an average five-fold cross-validated receiver operating characteristic area under the curve (ROC-AUC) of 0.85 ± 0.07, and the GCNN substrate models have an average ROC-AUC of 0.79 ± 0.08. We implemented the models along with applicability domain calculations in an easy-to-use web interface and made it publicly available at https://monstrous.bhsai.org/.

The role of surface modification in the interactions between CdTe quantum dots and ABC transporters in lung cancer cells

This paper aimed to investigate the role of surface modification on the potential interaction between CdTe quantum dots (QDs) and ABC transporters in doxorubicin-resistant lung cancer (A549/DOX) cells. For this purpose, CdTe QDs modified with -glutathione (GSH), -carboxy (COOH), and -amino (NH2) were applied, with the former two QDs exhibiting negative potentials and the latter ones being positive. All the three QDs reduced cell viability in a concentration-dependent manner, with NH2-CdTe QDs being more toxic. Such phenomena might be due to the adherence of NH2-CdTe QDs to negative cell membrane and thereby causing an enhanced accumulation. Addition of transporter inhibitors significantly enhanced the intracellular accumulation and toxicity of negative QDs, but such phenomena were barely found or even reversed for NH2-CdTe QDs, indicating that ABC transporters mainly excreted negative QDs. All the QDs caused little effects on the mRNA expression of ABC transporters, which should be due to the fact that the induction effects of QDs have been attenuated by the disruption of cell membrane. Overall, these results reveal the mechanism by which ABC transporters are involved in the efflux of CdTe QDs with different surface modifications, which could help the detoxification of QDs in the environment.

Transportation engineering for enhanced production of plant natural products in microbial cell factories

Plant natural products (PNPs) exhibit a wide range of biological activities and have essential applications in various fields such as medicine, agriculture, and flavors. Given their natural limitations, the production of high-value PNPs using microbial cell factories has become an effective alternative in recent years. However, host metabolic burden caused by its massive accumulation has become one of the main challenges for efficient PNP production. Therefore, it is necessary to strengthen the transmembrane transport process of PNPs. This review introduces the discovery and mining of PNP transporters to directly mediate PNP transmembrane transportation both intracellularly and extracellularly. In addition to transporter engineering, this review also summarizes several auxiliary strategies (such as small molecules, environmental changes, and vesicles assisted transport) for strengthening PNP transportation. Finally, this review is concluded with the applications and future perspectives of transportation engineering in the construction and optimization of PNP microbial cell factories.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning

The movement of organic anionic drugs across cell membranes is partly governed by interactions with SLC and ABC transporters in the intestine, liver, kidney, blood-brain barrier, placenta, breast, and other tissues. Major transporters involved include organic anion transporters (OATs, SLC22 family), organic anion transporting polypeptides (OATPs, SLCO family), and multidrug resistance proteins (MRPs, ABCC family). However, the sets of molecular properties of drugs that are necessary for interactions with OATs (OAT1, OAT3) vs. OATPs (OATP1B1, OATP1B3) vs. MRPs (MRP2, MRP4) are not well-understood. Defining these molecular properties is necessary for a better understanding of drug and metabolite handling across the gut-liver-kidney axis, gut-brain axis, and other multi-organ axes. It is also useful for tissue targeting of small molecule drugs and predicting drug-drug interactions and drug-metabolite interactions. Here, we curated a database of drugs shown to interact with these transporters in vitro and used chemoinformatic approaches to describe their molecular properties. We then sought to define sets of molecular properties that distinguish drugs interacting with OATs, OATPs, and MRPs in binary classifications using machine learning and artificial intelligence approaches. We identified sets of key molecular properties (e.g., rotatable bond count, lipophilicity, number of ringed structures) for classifying OATs vs. MRPs and OATs vs. OATPs. However, sets of molecular properties differentiating OATP vs. MRP substrates were less evident, as drugs interacting with MRP2 and MRP4 do not form a tight group owing to differing hydrophobicity and molecular complexity for interactions with the two transporters. If the results also hold for endogenous metabolites, they may deepen our knowledge of organ crosstalk, as described in the Remote Sensing and Signaling Theory. The results also provide a molecular basis for understanding how small organic molecules differentially interact with OATs, OATPs, and MRPs.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.