Predicting ligand interactions with ABC transporters in ADME

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

Transporter-mediated drug-drug interactions: advancement in models, analytical tools, and regulatory perspective

Drug-drug interactions mediated by transporters are a serious clinical concern hence a tremendous amount of work has been done on the characterization of the transporter-mediated proteins in humans and animals. The underlying mechanism for the transporter-mediated drug-drug interaction is the induction or inhibition of the transporter which is involved in the cellular uptake and efflux of drugs. Transporter of the brain, liver, kidney, and intestine are major determinants that alter the absorption, distribution, metabolism, excretion profile of drugs, and considerably influence the pharmacokinetic profile of drugs. As a consequence, transporter proteins may affect the therapeutic activity and safety of drugs. However, mounting evidence suggests that many drugs change the activity and/or expression of the transporter protein. Accordingly, evaluation of drug interaction during the drug development process is an integral part of risk assessment and regulatory requirements. Therefore, this review will highlight the clinical significance of the transporter, their role in disease, possible cause underlying the drug-drug interactions using analytical tools, and update on the regulatory requirement. The recent in-silico approaches which emphasize the advancement in the discovery of drug-drug interactions are also highlighted in this review. Besides, we discuss several endogenous biomarkers that have shown to act as substrates for many transporters, which could be potent determinants to find the drug-drug interactions mediated by transporters. Transporter-mediated drug-drug interactions are taken into consideration in the drug approval process therefore we also provided the extrapolated decision trees from in-vitro to in-vivo, which may trigger the follow-up to clinical studies.

C@PA: Computer-Aided Pattern Analysis to Predict Multitarget ABC Transporter Inhibitors

Based on literature reports of the last two decades, a computer-aided pattern analysis (C@PA) was implemented for the discovery of novel multitarget ABCB1 (P-gp), ABCC1 (MRP1), and ABCG2 (BCRP) inhibitors. C@PA included basic scaffold identification, substructure search and statistical distribution, as well as novel scaffold extraction to screen a large virtual compound library. Over 45,000 putative and novel broad-spectrum ABC transporter inhibitors were identified, from which 23 were purchased for biological evaluation. Our investigations revealed five novel lead molecules as triple ABCB1, ABCC1, and ABCG2 inhibitors. C@PA is the very first successful computational approach for the discovery of promiscuous ABC transporter inhibitors.

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

In vitro and in silico evaluation of P-glycoprotein inhibition through 99m Tc-methoxyisobutylisonitrile uptake

P-glycoprotein (P-gp) is a multidrug resistance (MDR) transporter with unknown structural details. This macromolecule is normally responsible for extruding xenobiotics from normal cells. Overexpression of P-gp in tumor cells is a major obstacle in cancer chemotherapy. In this study, human 3D model of P-gp was built by homology modeling based on mouse P-gp crystallographic structure and stabilized through 1 ns molecular dynamics (MD) simulation. Stabilized human P-gp structure was used for flexible docking of 80 drugs into the putative active site of P-gp. Accordingly, digoxin, itraconazole, risperidone, ketoconazole, prazosin, verapamil, cyclosporine A, and ranitidine were selected for further in vitro assay. Subsequently, cell-based P-gp inhibition assay was performed on Caco-2 cells while ^99m Tc-methoxyisobutylisonitrile (MIBI) was used as a P-gp efflux substrate for calculating IC50 values. Results of the ^99m Tc-MIBI uptake in drug-treated Caco-2 cells were in agreement with the previously reported activities. This study for the first time described the relation between molecular dynamics and flexible docking with cellular experiments using ^99m Tc-MIBI radiotracer for evaluation of potencies of P-gp inhibitors. Finally, results showed that our radiotracer-cell-based assay is an accurate and fast screening tool for detecting P-gp inhibitors and non-inhibitors in drug development process.

Tyrosine kinase inhibitors enhanced the efficacy of conventional chemotherapeutic agent in multidrug resistant cancer cells

Multidrug resistance (MDR) triggered by ATP binding cassette (ABC) transporter such as ABCB1, ABCC1, ABCG2 limited successful cancer chemotherapy. Unfortunately, no commercial available MDR modulator approved by FDA was used in clinic. Tyrosine kinase inhibitors (TKIs) have been administrated to fight against cancer for decades. Almost TKI was used alone in clinic. However, drug combinations acting synergistically to kill cancer cells have become increasingly important in cancer chemotherapy as an approach for the recurrent resistant disease. Here, we summarize the effect of TKIs on enhancing the efficacy of conventional chemotherapeutic drug in ABC transporter-mediated MDR cancer cells, which encourage to further discuss and study in clinic.

Biophysical Approaches Facilitate Computational Drug Discovery for ATP-Binding Cassette Proteins

Although membrane proteins represent most therapeutically relevant drug targets, the availability of atomic resolution structures for this class of proteins has been limited. Structural characterization has been hampered by the biophysical nature of these polytopic transporters, receptors, and channels, and recent innovations to in vitro techniques aim to mitigate these challenges. One such class of membrane proteins, the ATP-binding cassette (ABC) superfamily, are broadly expressed throughout the human body, required for normal physiology and disease-causing when mutated, yet lacks sufficient structural representation in the Protein Data Bank. However, recent improvements to biophysical techniques (e.g., cryo-electron microscopy) have allowed for previously “hard-to-study” ABC proteins to be characterized at high resolution, providing insight into molecular mechanisms-of-action as well as revealing novel druggable sites for therapy design. These new advances provide ample opportunity for computational methods (e.g., virtual screening, molecular dynamics simulations, and structure-based drug design) to catalyze the discovery of novel small molecule therapeutics that can be easily translated from computer to bench and subsequently to the patient's bedside. In this review, we explore the utility of recent advances in biophysical methods coupled with well-established in silico techniques towards drug development for diseases caused by dysfunctional ABC proteins.

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

Discovery of 4-acetyl-3-(4-fluorophenyl)-1-(p-tolyl)-5-methylpyrrole as a dual inhibitor of human P-glycoprotein and Staphylococcus aureus Nor A efflux pump

Polysubstituted pyrrole natural products, lamellarins, are known to overcome multi-drug resistance in cancer via the inhibition of p-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) efflux pumps. Herein, a series of simplified polysubstituted pyrroles, prepared via a one-pot domino protocol, were screened for P-gp inhibition in P-gp overexpressing human adenocarcinoma LS-180 cells using a rhodamine 123 efflux assay. Several compounds showed the significant inhibition of P-gp at 50 μM, as indicated by increase in the intracellular accumulation of Rh123 in LS-180 cells. Furthermore, pyrrole 5i decreased the efflux of digoxin, a FDA approved P-gp substrate in MDCK-MDR1 cells with an IC50 of 11.2 μM. In in vivo studies, following the oral administration of a P-gp substrate drug, rifampicin, along with compound , the Cmax and AUC0-∞ of rifampicin was enhanced by 31% and 46%, respectively. All the compounds were then screened for their ability to potentiate ciprofloxacin activity via the inhibition of Staphylococcus aureus Nor A efflux pump. Pyrrole showed the significant inhibition of S. aureus Nor A efflux pump with 8- and 4-fold reductions in the MIC of ciprofloxacin at 50 and 6.25 μM, respectively. The molecular docking studies of compound with the human P-gp and S. aureus Nor A efflux pump identified its plausible binding site and key interactions. Thus, the results presented herein strongly indicate the potential of this scaffold for its use as multi-drug resistance reversal agent or bioavailability enhancer.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Prediction of drug-ABC-transporter interaction--Recent advances and future challenges

With the discovery of P-glycoprotein (P-gp), it became evident that ABC-transporters play a vital role in bioavailability and toxicity of drugs. They prevent intracellular accumulation of toxic compounds, which renders them a major defense mechanism against xenotoxic compounds. Their expression in cells of all major barriers (intestine, blood–brain barrier, blood–placenta barrier) as well as in metabolic organs (liver, kidney) also explains their influence on the ADMET properties of drugs and drug candidates. Thus, in silico models for the prediction of the probability of a compound to interact with P-gp or analogous transporters are of high value in the early phase of the drug discovery process. Within this review, we highlight recent developments in the area, with a special focus on the molecular basis of drug–transporter interaction. In addition, with the recent availability of X-ray structures of several ABC-transporters, also structure-based design methods have been applied and will be addressed.

Generation of Broad-Spectrum Antifungal Drug Candidates from the Natural Product Compound Aureobasidin A

The natural product aureobasidin A (AbA) is a potent, well-tolerated antifungal agent with robust efficacy in animals. Although native AbA is active against a number of fungi, it has little activity against Aspergillus fumigatus, an important human pathogen, and attempts to improve the activity against this organism by structural modifications have to date involved chemistries too complex for continued development. This report describes novel chemistry for the modification of AbA. The key step involves functionalization of the phenylalanine residues in the compound by iridium-catalyzed borylation. This is followed by displacement of the pinacol boron moiety to form the corresponding bromide or iodide and substitution by Suzuki biaryl coupling. The approach allows for synthesis of a truly wide range of derivatives and has produced compounds with A. fumigatus minimal inhibitory concentrations (MIC) of <0.5 μg/mL. The approach is readily adaptable to large-scale synthesis and industrial production.

Homology modelling and molecular docking of MDR1 with chemotherapeutic agents in non-small cell lung cancer

MDR1, a protein commonly involved in drug transport, has been linked to multi drug resistance and disease progression in cancers such as non-small cell lung cancer. Hence, targeting this protein is essential for improving drug design and preventing adverse drug-drug interactions. The aim of the study was to examine chemotherapeutic drug binding to MDR1 and the interactions therein. We have used Schrödinger suite 2014, to perform homology modelling of human MDR1 based on Mouse MDR1, followed by Induced Fit Docking with Paclitaxel, Docetaxel, Gemcitabine, Carboplatin and Cisplatin drugs. Finally, we evaluated drug binding affinities using Prime/MMGBSA and using these scores we compared the affinities of combination therapies against MDR1. Analysis of the docking results showed Paclitaxel>Docetaxel>Gemcitabine>Carboplatin>Cisplatin as the order of binding affinities, with Paclitaxel having the best docking score. The combination drug binding affinity analysis showed Paclitaxel+Gemcitabine to have the best docking score and hence, efficacy. Through our investigation we have identified the residues Gln 195 and Gln 946 to be more frequently involved in drug binding interactions with MDR1. Our results suggest that, Paclitaxel or combination of Paclitaxel+Gemcitabine could serve as a suitable therapy against MDR1 in NSCLC patients. Thus, our study provides new insight into the possible repurposing of chemotherapeutic drugs in targeting elevated MDR1 levels in NSCLC patients, thereby ensuring better overall outcome. Further our study highlights the use of in silico methodologies in understanding drug binding to protein targets and its relevance to advancing lung cancer therapy.

Modelling three-dimensional protein structures for applications in drug design

A structural perspective of drug target and anti-target proteins, and their molecular interactions with biologically active molecules, largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimisation. In the absence of experimental 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homology modelling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estimation deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately determines its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modelling practice are also provided.

Osthol and curcumin as inhibitors of human Pgp and multidrug efflux pumps of Staphylococcus aureus: reversing the resistance against frontline antibacterial drugs

Curcumin and osthol are identified as NorA pump inhibitors.

Overview of experimental models of the blood-brain barrier in CNS drug discovery

The blood-brain barrier (BBB) is a physical and metabolic entity that isolates the brain from the systemic circulation. The barrier consists of tight junctions between endothelial cells that contain egress transporters and catabolic enzymes. To cross the BBB, a drug must possess the appropriate physicochemical properties to achieve a sufficient time-concentration profile in brain interstitial fluid (ISF). In this overview, we review techniques to measure BBB permeation, which is evidenced by the free concentration of compound in brain ISF over time. We consider a number of measurement techniques, including in vivo microdialysis and brain receptor occupancy following perfusion. Consideration is also given to the endothelial and nonendothelial cell systems used to assess both the BBB permeation of a test compound and its interactions with egress transporters, and computer models employed for predicting passive permeation and the probability of interactions with BBB transporters.

Development of conformation independent computational models for the early recognition of breast cancer resistance protein substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked to multidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous system conditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to random subsamples of Dragon molecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.

Overview and introduction: the blood-brain barrier in health and disease

This article introduces the special issue on "Blood-Brain Barrier and Epilepsy." We review briefly current understanding of the structure and function of the blood-brain barrier (BBB), including its development and normal physiology, and ways in which it can be affected in pathology. The BBB formed by the endothelium of cerebral blood vessels is one of three main barrier sites protecting the central nervous system (CNS). The barrier is not a rigid structure, but a dynamic interface with a range of interrelated functions, resulting from extremely effective tight junctions, transendothelial transport systems, enzymes, and regulation of leukocyte permeation, which thereby generates the physical, transport, enzymatic, and immune regulatory functions of the BBB. The brain endothelial cells are important components of a "modular" structure, the neurovascular unit (NVU), with several associated cell types and extracellular matrix components. Modern methods have helped in identifying a range of proteins involved in barrier structure and function, and recent studies have revealed important stages, cell types, and signaling pathways important in BBB development. There is a growing list of CNS pathologies showing BBB dysfunction, with strong evidence that this can play a major role in certain disease etiologies. The articles that follow in this issue summarize in more detail reports and discussions of the recent international meeting on "BBB in Neurological Dysfunctions," which took place recently at Ben-Gurion University of the Negev Desert Campus (Beer-Sheva, Israel), focusing on the link between experimental and clinical studies, and the ways in which these lead to improved drug treatments.

Challenges in the Development of Functional Assays of Membrane Proteins

Lipid bilayers are natural barriers of biological cells and cellular compartments. Membrane proteins integrated in biological membranes enable vital cell functions such as signal transduction and the transport of ions or small molecules. In order to determine the activity of a protein of interest at defined conditions, the membrane protein has to be integrated into artificial lipid bilayers immobilized on a surface. For the fabrication of such biosensors expertise is required in material science, surface and analytical chemistry, molecular biology and biotechnology. Specifically, techniques are needed for structuring surfaces in the micro- and nanometer scale, chemical modification and analysis, lipid bilayer formation, protein expression, purification and solubilization, and most importantly, protein integration into engineered lipid bilayers. Electrochemical and optical methods are suitable to detect membrane activity-related signals. The importance of structural knowledge to understand membrane protein function is obvious. Presently only a few structures of membrane proteins are solved at atomic resolution. Functional assays together with known structures of individual membrane proteins will contribute to a better understanding of vital biological processes occurring at biological membranes. Such assays will be utilized in the discovery of drugs, since membrane proteins are major drug targets.

Enhancing chemosensitivity in ABCB1- and ABCG2-overexpressing cells and cancer stem-like cells by an Aurora kinase inhibitor CCT129202

Imidazopyridine CCT129202 is an inhibitor of Aurora kinase activity and displays a favorable antineoplastic effect in preclinical studies. Here, we investigated the enhanced effect of CCT129202 on the cytotoxicity of chemotherapeutic drugs in multidrug resistant (MDR) cells with overexpression of ATP-binding cassette (ABC) transporters and cancer stem-like cells. CCT129202 of more than 90% cell survival concentration significantly enhanced the cytotoxicity of substrate drugs and increased the intracellular accumulations of doxorubicin and rhodamine 123 in ABCB1 and ABCG2 overexpressing cells, while no effect was found on parental sensitive cells. Interestingly, CCT129202 also potentiated the sensitivity of cancer stem-like cells to doxorubicin. Importantly, CCT129202 increased the inhibitory effect of vincristine and paclitaxel on ABCB1 overexpressing KBv200 cell xenografts in nude mice and human esophageal cancer tissue overexpressing ABCB1 ex vivo, respectively. Furthermore, the ATPase activity of ABCB1 was inhibited by CCT129202. Homology modeling predicted the binding conformation of CCT129202 within the large hydrophobic cavity of ABCB1. On the other hand, CCT129202 neither apparently altered the expression levels of ABCB1 and ABCG2 nor inhibited the activity of Aurora kinases in MDR cells under the concentration of reversal MDR. In conclusion, CCT129202 significantly reversed ABCB1- and ABCG2-mediated MDR in vitro, in vivo and ex vivo by inhibiting the function of their transporters and enhanced the eradication of cancer stem-like cells by chemotherapeutic agents. CCT129202 may be a candidate as MDR reversal agent for antineoplastic combination therapy and merits further clinical investigation.

The dynamics of drug resistance: a mathematical perspective

Resistance to chemotherapy is a key impediment to successful cancer treatment that has been intensively studied for the last three decades. Several central mechanisms have been identified as contributing to the resistance. In the case of multidrug resistance (MDR), the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. Mathematical models of drug resistance have dealt with many of the known aspects of this field, such as pharmacologic sanctuary and location/diffusion resistance, intrinsic resistance, induced resistance and acquired resistance. In addition, there are mathematical models that take into account the kinetic/phase resistance, and models that investigate intracellular mechanisms based on specific biological functions (such as ABC transporters, apoptosis and repair mechanisms). This review covers aspects of MDR that have been mathematically studied, and explains how, from a methodological perspective, mathematics can be used to study drug resistance. We discuss quantitative approaches of mathematical analysis, and demonstrate how mathematics can be used in combination with other experimental and clinical tools. We emphasize the potential benefits of integrating analytical and mathematical methods into future clinical and experimental studies of drug resistance.

Utility of protein structures in overcoming ADMET-related issues of drug-like compounds

The number of solved X-ray structures of proteins relevant for ADMET processes of drug molecules has increased remarkably over recent years. In principle, this development offers the possibility to complement the quantitative structure-property relationship (QSPR)-dominated repertoire of in silico ADMET methods with protein-structure-based approaches. However, the complex nature and the weak nonspecific ligand-binding properties of ADMET proteins take structural biology methods and current docking programs to the limit. In this review we discuss the utility of protein-structure-based design and docking approaches aimed at overcoming issues related to plasma protein binding, active transport via P-glycoprotein, hERG channel mediated cardiotoxicity and cytochrome P450 inhibition, metabolism and induction.

Overcoming tumor multidrug resistance using drugs able to evade P-glycoprotein or to exploit its expression

Multidrug resistance (MDR) is a major obstacle to the effective treatment of cancer. Cellular overproduction of P-glycoprotein (P-gp), which acts as an efflux pump for various anticancer drugs (e.g. anthracyclines, Vinca alkaloids, taxanes, epipodophyllotoxins, and some of the newer antitumor drugs) is one of the more relevant mechanisms underlying MDR. P-gp belongs to the superfamily of ATP-binding cassette transporters and is encoded by the ABCB1 gene. Its overexpression in cancer cells has become a therapeutic target for circumventing MDR. As an alternative to the classical pharmacological strategy of the coadministration of pump inhibitors and cytotoxic substrates of P-gp and to other approaches applied in experimental tumor models (e.g. P-gp-targeting antibodies, ABCB1 gene silencing strategies, and transcriptional modulators) and in the clinical setting (e.g. incapsulation of P-gp substrate anticancer drugs into liposomes or nanoparticles), a more intriguing strategy for circumventing MDR is represented by the development of new anticancer drugs which are not substrates of P-gp (e.g. epothilones, second- and third-generation taxanes and other microtubule modulators, topoisomerase inhibitors). Some of these drugs have already been tested in clinical trials and, in most of cases, show relevant activity in patients previously treated with anticancer agents which are substrates of P-gp. Of these drugs, ixabepilone, an epothilone, was approved in the United States for the treatment of breast cancer patients pretreated with an anthracycline and a taxane. Another innovative approach is the use of molecules whose activity takes advantage of the overexpression of P-gp. The possibility of overcoming MDR using the latter two approaches is reviewed herein.