Classification of P-glycoprotein-interacting compounds using machine learning methods

P-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 noninhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance

Computer-Aided Recognition of ABC Transporters Substrates and Its Application to the Development of New Drugs for Refractory Epilepsy

Despite the introduction of more than 15 third generation antiepileptic drugs to the market from 1990 to the moment, about one third of the epileptic patients still suffer from refractory to intractable epilepsy. Several hypotheses seek to explain the failure of drug treatments to control epilepsy symptoms in such patients. The most studied one proposes that drug resistance might be related with regional overactivity of efflux transporters from the ATP-Binding Cassette (ABC) superfamily at the blood-brain barrier and/or the epileptic foci in the brain. Different strategies have been conceived to address the transporter hypothesis, among them inhibiting or down-regulating the efflux transporters or bypassing them through a diversity of artifices. Here, we review scientific evidence supporting the transporter hypothesis along with its limitations, as well as computer-assisted early recognition of ABC transporter substrates as an interesting strategy to develop novel antiepileptic drugs capable of treating refractory epilepsy linked to ABC transporters overactivity.Laboratorio de Investigación y Desarrollo de Bioactivo

Search for ABCB1 modulators among 2-amine-5-arylideneimidazolones as a new perspective to overcome cancer multidrug resistance

Multidrug resistance (MDR) is a severe problem in the treatment of cancer with overexpression of glycoprotein P (Pgp, ABCB1) as a reason for chemotherapy failure. A series of 14 novel 5-arylideneimidazolone derivatives containing the morpholine moiety, with respect to two different topologies (groups A and B), were designed and obtained in a three- or four-step synthesis, involving the Dimroth rearrangement. The new compounds were tested for their inhibition of the ABCB1 efflux pump in both sensitive (parental (PAR)) and ABCB1-overexpressing (MDR) T-lymphoma cancer cells in a rhodamine 123 accumulation assay. Their cytotoxic and antiproliferative effects were investigated by a thiazolyl blue tetrazolium bromide (MTT) assay. For active compounds, an insight into the mechanisms of action using either the luminescent Pgp-Glo™ Assay in vitro or docking studies to human Pgp was performed. The safety profile in vitro was examined. Structure–activity relationship (SAR) analysis was discussed. The most active compounds, representing both 2-substituted- (11) and Dimroth-rearranged 3-substituted (18) imidazolone topologies, displayed 1.38–1.46 fold stronger efflux pump inhibiting effects than reference verapamil and were significantly safer than doxorubicin in cell-based toxicity assays in the HEK-293 cell line. Results of mechanistic studies indicate that active imidazolones are substrates with increasing Pgp ATPase activity, and their dye-efflux inhibition via competitive action on the Pgp verapamil binding site was predicted in silico

Docking applied to the prediction of the affinity of compounds to p-glycoprotein

P-glycoprotein (P-gp) is involved in the transport of xenobiotic compounds and responsible for the decrease of the drug accumulation in multi-drug-resistant cells. In this investigation we compare several docking algorithms in order to find the conditions that are able to discriminate between P-gp binders and nonbinders. We built a comprehensive dataset of binders and nonbinders based on a careful analysis of the experimental data available in the literature, trying to overcome the discrepancy noticeable in the experimental results. We found that Autodock Vina flexible docking is the best choice for the tested options. The results will be useful to filter virtual screening results in the rational design of new drugs that are not expected to be expelled by P-gp.Facultad de Ciencias Exacta

Machine Learning Approaches for Improving Prediction Performance of Structure-Activity Relationship Models

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies. First, to improve the prediction accuracy of learning from imbalanced data, Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms combined with bagging as an ensemble strategy was evaluated. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that this method significantly outperformed other conventional methods. SMOTEENN with bagging became less effective when IR exceeded a certain threshold (e.g., \u3e40). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p \u3c 0.001, ANOVA) by 22-27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Lastly, current features used for QSAR based machine learning are often very sparse and limited by the logic and mathematical processes used to compute them. Transformer embedding features (TEF) were developed as new continuous vector descriptors/features using the latent space embedding from a multi-head self-attention. The significance of TEF as new descriptors was evaluated by applying them to tasks such as predictive modeling, clustering, and similarity search. An accuracy of 84% on the Ames mutagenicity test indicates that these new features has a correlation to biological activity. Overall, the findings in this study can be applied to improve the performance of machine learning based Quantitative Structure-Activity/Property Relationship (QSAR) efforts for enhanced drug discovery and toxicology assessments

Reversing multidrug resistance (MDR) in cancer cells by targeting p-glycoprotein (P-gp) : insights into the mechanism of MDR reversal from in silico P-gp modeling

Tese de doutoramento, Farmácia (Química Farmacêutica e Terapêutica), Universidade de Lisboa, Faculdade de Farmácia, 2017Multidrug resistance (MDR) in cancer is one of the major impairments in the success of chemotherapy. The main objective of this work was the identification and optimization of MDR reversers, derived from Euphorbia species, and to gain insights on the drug efflux mechanism by P-gp. The phytochemical study of Euphorbia pedroi yielded four new diterpenes, two macrocyclic lathyranes (9, 12), one jatrophane (10) and an unprecedented rearranged tigliane (13). While 9 is characterized by a rare double α,β-unsaturated ketone system, 13 has a new skeleton that may result from a pinacol rearrangement as proposed in a possible biogenetic pathway. Furthermore, a new spiroterpenoid (6) was also isolated, together with several known terpenoids (1-5, 7, 8, 14-16) and flavonoids (17-18). Molecular derivatization of compounds 15 and 17 yielded two set of new derivatives (19-24 and 25-71, respectively). In this way, reaction of 15 with hydroxylamine hydrochloride gave compound 19 that was further acylated with acyl anhydrides (20) and chlorides (21-24). Flavanone derivatives were obtained through three main approaches. Firstly, the methylation of naringenin (17) yielded compounds 46 and 47. Following, while hydrazones (25-28, 48-53) and carbohydrazides (37, 38, 40-42, 54-63) were obtained from compounds 17, 46 and 47, azines (29-36) were prepared by the reaction of 28 with aldehydes. A thiosemicarbazone derivative (39) was also prepared from 17. Other flavanone derivatives were additionally synthesized through a Mannich-type reaction (43-45) or by alkylation of compound 47 with epichlorohydrin (64, 65) followed by the reaction with amines, indole or thiophenol to yield 66-71. The chemical structures of all compounds were deduced from physical and spectroscopic data (IR, MS, 1D- and 2D-NMR experiments). The P-gp-mediated MDR reversal activity of compounds was evaluated by combining transport and chemosensitivity assays, in mouse lymphoma L5178Y-MDR (1-71) and Colo320 (1-18) cell models. While 6 showed high modulation activity even at 0.2 μM, compound 9 combined a good P-gp modulatory activity with a strong cytotoxic effect in both cell lines. When compared to the parent compound (15), the derivatives 20 and 22 and 23 were stronger efflux modulators towards the L5178Y-MDR cells. Most of the flavanone derivatives (25-71) were also more active than the parent compound (17) in L5178Y-MDR cells, being the most significant results observed for propanolamines 66-69, where compound 69 was found to be a strong P-gp modulator even at 2.0 μM. When in combination with doxorubicin, the natural compounds 6, 9, 10, 12 and 13 synergistically enhanced the cytotoxic effects of the drug. Strong synergistic effects were also observed for the derivatives 22 and 69. The ability of compounds 25-45 to modulate drug efflux by MRP1 and BCRP was also assessed, using human MRP1- and BCRP-transfected cell models. For this set of compounds, a second P-gp-transfected cell model was used. Azines (29-36) displayed significant activity towards BCRP while hydrazides (38-42) showed a good selectivity profile for MRP1. Oppositely, derivatives 35 and 36 displayed a good activity profile in both efflux pumps, when tested at 20 μM. Based on these results, new structure-activity relationships (SAR) for the selective BCRP and MRP1 inhibitors were obtained, unveiling which structural features could be directly correlated with the observed biological activity. The efflux mechanism of P-gp was studied by means of molecular dynamics and docking studies. The ‘linker’ polypeptide sequence was found to be important to absorb stronger motions and acting as a ‘damper’ between both NBDs, stabilizing the cytosolic portion of the transporter. Following, based on a previously refined P-gp structure, three distinct drugbinding sites could be identified and characterized, in a good agreement with published experimental data. Together with a new classification scheme, cross interactions between the substrate/modulator and each halve of P-gp were identified as an important mechanism in efflux modulation. Drug transit from bulk water into the DBP was also characterized as an overall free-energy downhill process, with no activation energy required for crossing the gate found between transmembrane helices 10 and 12. Furthermore, from the analysis on drug adsorption to the cytoplasmic domains in P-gp substrates and modulators were show to have different free energies of adsorption in both lipid/water and protein/ water interfaces and important differences in drug–protein interactions, protein dynamics and membrane biophysical characteristics were observed between non-substrates, substrates and modulators.A resistência a múltiplos fármacos (MDR) no cancro configura-se como um dos principais problemas que atualmente comprometem o sucesso dos regimes de quimioterapia. Dos mecanismos celulares envolvidos na MDR, um dos mais importantes consiste no aumento do efluxo de citotóxicos ou de sequestração intracelular devido à sobre-expressão de transportadores da família ABC, nomeadamente a glicoproteína-P (P-gp), a proteína associada à multirresistência 1 (MRP1) e a proteína de resistência do cancro da mama (BCRP). Envolvidas em fenómenos normais de destoxificação celular, estas bombas de efluxo encontram-se igualmente implicadas na redução da concentração intracelular de fármacos antitumorais, transportando-os contra o seu gradiente de concentração, através da utilização da energia gerada pela ligação e hidrólise do ATP. Apesar das três gerações de moduladores da P-gp já desenvolvidas, nenhum modulador foi clinicamente eficaz na reversão da MDR quando em coadministração com fármacos citotóxicos. No entanto, e uma vez que a procura de fármacos capazes de reverter a MDR continua a ser uma das abordagens mais promissoras, novas moléculas isoladas de fontes naturais são atualmente consideradas como uma possível quarta geração de moduladores de bombas de efluxo, atuando como reversores da MDR em células tumorais. Assim, um dos objetivos principais deste trabalho foi a identificação e otimização de novos reversores da MDR, isolados a partir da espécie Euphorbia pedroi ou obtidos através de derivatização química de compostos isolados em grandes quantidades. O estudo fitoquímico da E. pedroi permitiu o isolamento de quatro novos diterpenos, dois latiranos (9, 12), um jatrofano (10) e um tigliano rearranjado com um esqueleto novo (13). Enquanto a pedrodiona A (9) é caracterizada pela presença de dois sistemas α,β-insaturados, o pedrolido (13) apresenta um rearranjo de pinacol em C-6/C-7 incomum. Foi também isolado um esteroide novo designado por spiropedroxodiol (6), contendo um esqueleto spiro raro, conjuntamente com vários terpenoides (1-5, 7, 8, 14-16) e flavonoides conhecidos (17-18). Por forma a otimizar as propriedades moduladoras do helioscopinolido E (15) e da naringenina (17), foram preparados dois conjuntos de compostos com o núcleo do ent-abietano (19-24) e da flavanona (25-71) através da derivatição molecular de vários grupos funcionais. Enquanto que no primeiro caso a reação do composto 15 com hidroxilamina deu origem à oxima 19 (C=N-OH) e posteriormente aos compostos 20-24 por acilação com anidridos ou cloretos de ácido, os derivados do núcleo da flavanona foram obtidos através de três abordagens distintas. Inicialmente, a metilação dos hidroxilos da naringenina (17) nas posições C-7 e C-4’ originou a sakuranetina (47) e a 4’-metoxisakuranetina (48). Em seguida, enquanto que as hidrazonas 25-28, 48-53 (C=N-NH-R) e as carbohidrazidas 37, 38, 40-42, 54-63 (C=N-NHCO-R) foram preparadas a partir dos compostos 17, 46 e 47, as azinas 29-36 (C=N-N=CH-R) foram sintetizadas através da reação do composto 28 (C=N-NH2) com aldeídos. Foi também sintetizada uma tiosemicarbazona (39) através da reação da naringenina (17) com a N,Ndimetiltio- semicarbazida. Foram ainda preparados outros derivados do núcleo da flavanona i) através de uma reação de Mannich nas posições C-6 e C-8 (43-45) e ii) através da alquilação do hidroxilo da posição C-4’ da sakuranetina (47) com epiclorohidrina (64, 65) seguida da reação com aminas, indole ou tiofenol para originar as correspondentes propanolaminas (66- 69) e os compostos 70-71. As estruturas químicas dos compostos foram deduzidas a partir dos seus dados físicos e espectroscópicos (IR, MS, 1D e 2D-RMN). A capacidade de reversão de MDR dos compostos foi avaliada através da combinação de ensaios funcionais com ensaios de quimiossensibilidade, utilizando como modelos as células de linfoma de rato L5178Y-MDR (1-71) e células Colo320 humanas (1-18). Enquanto que o spiropedroxodiol (6) demonstrou possuir uma elevada capacidade para modular o efluxo mesmo em concentrações submicromolares (0.2 μM), a atividade expressa pela pedrodiona A (8) combinou uma boa atividade na reversão de MDR com uma elevada citotoxicidade nas linhas celulares L5178Y-MDR (FAR 19.13, IC50 0.259 ± 1.05 μM) e Colo320 (FAR 1.52, IC50 0.822 ± 1.05 μM). Relativamente aos derivados obtidos a partir do composto 15, a acilação da oxima do heliscopinolido E (19) aumentou a capacidade moduladora do efluxo nas células de linfoma de rato na maioria dos compostos sintetizados. Nos derivados da naringenina (17), a metilação de hidroxilos fenólicos em conjunto com i) a substituição do grupo carbonilo na posição C-4 por hidrazonas (25-28, 48-53), azinas (29-36) ou carbohidrazidas (37-42, 54-63) aumentou a atividade de reversão de MDR dos compostos a 20 μM. Verificou-se também que a alquilação do hidroxilo na posição C-4’ para gerar as correspondentes propanolaminas (64-71) aumentou substancialmente as suas propriedades anti-MDR (66-69) a concentrações mais baixas (2.0 μM). Adicionalmente, e quando testados em combinação com a doxorubicina, todos os compostos testados (6, 10, 12, 13, 22, 69) exceto o 9 (efeito aditivo) e o 60 (antagonismo) potenciaram a atividade citotóxica quando em co-administração com o fármaco antitumoral. Os efeitos dos derivados 25-45 da flavanona (17) foram igualmente avaliados noutros transportadores ABC frequentemente sobre-expressos em células tumorais, como a proteína de resistência do cancro da mama (BCRP) e a proteína associada à multirresistência 1 (MRP1). Adicionalmente, estes compostos foram também testados numa linha celular alternativa, igualmente transfetada com o gene da P-gp (NIH/3T3). Assim, enquanto que as hidrazonas (25-36) demonstraram uma maior seletividade para a BCRP, os derivados de hidrazidas (37, 38, 40-42) foram seletivos para a MRP1. No entanto, importa referir que os compostos 35, 36 e 39 (uma tiosemicarbazona) demonstraram possuir uma atividade apreciável como modulador de efluxo em ambas as bombas MRP1 e BCRP (a 20 μM). Finalmente, e tendo como base os resultados acima descritos, foram desenvolvidas novas relações estrutura-atividade (SAR) em que a posição espacial do substituinte foi identificada como um dos principais fatores para a atividade registada na MRP1 e BCRP. Por fim, este estudo providenciou pela primeira vez um racional para o desenvolvimento de novos moduladores para a P-gp, BCRP e MRP1 a partir do núcleo da flavanona. A publicação da estrutura cristalográfica da P-gp murina, em 2009, colmatou uma importante falha no estudo das bombas de efluxo e permitiu um crescimento exponencial de estudos estruturais, visando um maior conhecimento sobre o mecanismo de efluxo pela P gp. Desta forma, e para evitar os problemas subjacentes ao desenvolvimento das primeiras três gerações de moduladores da MDR em que a estrutura do transportador não era conhecida, importa saber os principais passos pelos quais ocorre o efluxo de substratos e alguns detalhes específicos adicionais acerca do mecanismo de efluxo da P-gp Assim, foi estudado o mecanismo de efluxo pela P-gp através de dinâmica e docking molecular. A estrutura polipeptídica em falta (“linker”) foi determinada como essencial para a estabilização dos domínios citoplasmáticos da P-gp, atuando por forma a absorver fortes oscilações estruturais. Com base na estrutura previamente refinada da P-gp, foram identificados e caracterizados de acordo com dados experimentais publicados, três locais de ligação distintos, dois de ligação a substratos e um de ligação a moduladores. Através da publicação de um novo esquema de classificação, as interações cruzadas entre o modulador e cada domínio da P-gp (N-e C-terminais) foram identificadas como um mecanismo importante na modulação de efluxo. O processo biofísico pelo qual moléculas são capazes de permear a membrana a partir do citoplasma e a sua entrada na cavidade interna da P-gp foi também caracterizado como um processo energeticamente favorável, desprovido de barreiras energéticas, mesmo durante a passagem através das hélices transmembranares 10 e 12. Do mesmo modo, substratos e moduladores revelaram ter diferentes energias livres de adsorção em cada uma das interfaces (lípidos/água e proteína/água), tendo sido igualmente registadas diferenças importantes nas interações fármaco-proteína, nos processos dinâmicos do transportador e nas características biofísicas da membrana quando em contacto com não-substratos, substratos e moduladores

Ligand and Structure-Based Classification Models for Prediction of P‑Glycoprotein Inhibitors

The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization