New tylophorine analogs as potential antitumor agents

Tylophorine and related phenanthroindolizidine alkaloids isolated principally from Asclepiadaceae have been targets of synthetic modification because of their profound cytotoxic antitumor activity. As part of our interest in plant-derived antitumor agents, novel water-soluble phenanthrene-based tylophorine derivatives (PBTs) were designed, synthesized and evaluated for anticancer activity. Several PBTs showed superior activity profiles with EC50 values in the sub-micromolar range, which are comparable to those of currently used antitumor drugs. A structure-activity relationship (SAR) study was also explored to facilitate the further development of this new compound class. Subsequently, C9-substituted PBTs were designed and synthesized using 2, 3 methylenedioxy-6-methoxyphenanthrene as a common skeleton based on our prior SAR findings. The C-9 site is an ideal position for introducing more polar, water-solubility-enhancing moieties. We also extended the in vitro antitumor screening to include additional significant tumor types [A549 (lung), DU-145 (prostate), ZR-751 (breast), KB (nasopharyngeal)] as well as a multi-drug resistant cancer cell subline [KB-Vin (multi-drug resistant KB subline)]. Most of the compounds showed fairly uniform and potent cytotoxic activity with EC50 approximately equal to10-7 M against both wild type and matched multi-drug resistant KB cell lines, and displayed notable selectivity toward DU-145 (prostate) and ZR-751 (breast) cancer cell lines. A combination of QSAR modeling and database mining was used to facilitate further design and discovery of novel anticancer PBTs. MolConnZ 2D topological descriptors were applied to a dataset of 52 chemically diverse PBTs and variable selection models were generated using the kappa nearest neighbor (kappa-NN) method. The derived kappa-NN QSAR models have high internal accuracy, with leave-one-out cross-validated R2 (q2) values ranging between 0.6 and 0.8. The original dataset was then divided into several training and test sets to provide highly predictive models with q2 values greater than 0.5 for the training sets and R2 values greater than 0.6 for the test sets. The ten best models were capable of mining the commercially available ChemDiv Database (450,000 compounds) and resulted in 34 consensus hits. Of these 34 compounds, 10 compounds were tested and 8 were confirmed to be active with a best EC50 of 1.8 ?M. These models were further validated by predicting the activity of four new PBTs compounds with reasonable accuracy and 11 consensuses hits with R2 of 0.52. These results indicate that this approach can be successfully applied to further design and discovery of anticancer drug candidates from this compound class

Modelos bioinformáticos y estudio de receptores de proteínas mediante el uso de redes complejas para el desarrollo y diseño de fármacos eficaces en patologías del sistema nervioso central

La búsqueda y desarrollo de fármacos eficaces para el tratamiento de enfermedades neurodegenerativas ha generado grandes expectativas, debido a la relevancia que tienen sobre la economía de los sistemas sanitarios y la tremenda carga y desgaste que sufren familia y cuidadores. Por ello, la industria farmacéutica se ha volcado sobre estas patologías en las últimas tres décadas, pero las dificultades de realizar ensayos sobre el SN provoca que los gastos y tiempos de investigación se disparen, limitando de forma considerable la rentabilidad de los procesos tradicionales en el desarrollo de nuevos medicamentos. Es en este apartado donde realiza sus aportaciones el diseño de fármacos, dedicando una parte del mismo al desarrollo de modelos matemáticos que permitan predecir propiedades de interés para una gran variedad de sistemas químicos incluyendo moléculas de bajo peso molecular, polímeros, biopolímeros, sistemas heterogéneos, formulaciones farmacéuticas, conglomerados de moléculas e iones, materiales, nano-estructuras y otros. En dicho sentido, los estudios QSAR (Quantitative Structure-Activity-Relationships) son usados cada vez mas como herramientas para el descubrimiento molecular. Estos modelos QSAR pueden ser diseñados para que predigan la probabilidad de que un fármaco sea efectivo contra una enfermedad degenerativa determinada ya sea la enfermedad de Parkinson, Alzheimer o cualquier otra, actuando sobre una diana molecular específica. En esta memoria presentamos de manera conjunta la revisión de modelos previos y trabajos específicos novedosos, en los que se han introducido nuevos índices numéricos utilizados para describir tanto la estructura molecular de fármacos como la estructura macromolecular de sus dianas o receptores (proteínas y/o ADN/ARN). Con estos ITs hemos sido capaces de desarrollar nuevos modelos multiQSAR de gran interés por su doble función en la predicción de fármacos y sus dianas moleculares. Estos trabajos permitirán la introducción de nuevos conceptos teóricos y la evolución hacia modelos con posibles aplicaciones en la búsqueda de nuevos fármacos neuroprotectores útiles en el tratamiento de las enfermedades de Parkinson y Alzheimer y/o nuevas dianas moleculares para estos fármacos. Este tipo de investigación abarca un área general-básica en la que interactúan la Bioinformática y la Quimioinformática

Computer-aided approaches in drug design: the exigent way forward: dynamic perspectives into the mechanistic activities of small molecule inhibitors toward antiviral, antitubercular and anticancer therapeutic interventions.

Doctoral Degree. University of KwaZulu-Natal, Durban.The crucial role of CADD in the drug design process is now indisputable and has proven over the years that it can accelerate the discovery potential drug candidates while reducing the associated cost. Using knowledge and information about biological target or knowledge about a ligand with proven bioactivity, CADD, and its techniques can influence various drug discovery pipeline stages. The ability CADD approaches to elucidate drug-target interactions at the atomistic level allows for investigations of the mechanism of drugs' actions, revealing atomistic insights that influence drug design and improvement. CADD approaches also seek to augment traditional in vitro and in vivo experimental techniques and not replace them since CADD approaches can also allow modeling complex biological processes that hitherto seemed impossible to explore using experimental methods. According to the World Health Organization (WHO), featuring prominently in the top ten causes of death are cancer, lower respiratory tract infection, tuberculosis (TB), and viral infections such as HIV/AIDS. Collectively, these diseases are of global health concerns, considering a large number of associated deaths yearly. Over the years, several therapeutic interventions have been employed to treat, manage, or cure these diseases, including chemotherapy, surgery, and radiotherapy. Of these options, small molecule inhibitors have constituted an integral component in chemotherapy, thereby undoubtedly playing an essential role in patient management. Although significant success has been achieved using existing therapeutic approaches, the emergence of drug resistance and the challenges of associated adverse side effects has prompted the need for the drug design processes against these diseases to remain innovative, including combining existing drugs and establishing improved therapeutic options that could overcome resistance while maintaining minimal side effects to patients. Therefore, an exploration of drug target interactions towards unraveling mechanisms of actions as performed in the reports in this thesis are relevant since the molecular mechanism provided could form the basis for the design and identification of new therapeutic agents, improvement of the therapeutic activity of existing drugs, and also aid in the development of novel therapeutic strategies against these diseases of global health concern. Therefore the studies in this thesis employed CADD approaches to investigates molecular mechanisms of actions of novel therapeutic strategies directed towards some crucial therapeutics implicated in viral infections, tuberculosis, and cancer. Therapeutic targets studied included; SARS-CoV-2 RNA dependent RNA polymerase (SARS-CoV-2 RdRp), Human Rhinovirus B14 (HRV-B14) and human N-myristoyltransferases in viral infections, Dihydrofolate reductase (DHFR) and Flavin-dependent thymidylate synthase (FDTS) in TB, human variants of TCRCD1d, and Protein Tyrosine Phosphatase Receptor Zeta (PTPRZ) in cancer. The studies in this thesis is divided into three domains and begins with a thorough review of the concept of druggability and drug-likeness since the crux of the subsequent reports revolved around therapeutic targets and their inhibitions by small molecule inhibitors. This review highlights the principles of druggability and drug-likeness while detailing the recent advancements in drug discovery. The review concludes by presenting the different computational, highlighting their reliability for predictive analysis. In the first domain of the research, we sought to unravel the inhibitory mechanism of some small molecule inhibitors against some therapeutic targets in viral infections by explicitly focusing on the therapeutic targets; SARS-CoV-2 RdRp, HRV-B14, and N-myristoyltransferase. Therapeutic targeting of SARS-CoV-2 RdRp has been extensively explored as a viable approach in the treatment of COVID-19. By examining the binding mechanism of Remdesivir, which hitherto was unclear, this study sought to unravel the structural and conformational implications on SARS-CoV-2 RdRp and subsequently identify crucial pharmacophoric moieties of Remdesivir required for its inhibitory potency. Computational analysis showed that the modulatory activity of Remdesivir is characterized by an extensive array of high-affinity and consistent molecular interactions with specific active site residues that anchor Remdemsivir within the binding pocket for efficient binding. Results also showed that Remdesivir binding induces minimal individual amino acid perturbations, subtly interferes with deviations of C-α atoms, and restricts the systematic transition of SARS-CoV-2 RdRp from the “buried” hydrophobic region to the “surface exposed” hydrophilic region. Based on observed high-affinity interactions with SARS-CoV-2 RdRp, a pharmacophore model was generated, which showcased the crucial functional moieties of Remdesivir. The pharmacophore was subsequently employed for virtual screening to identify potential inhibitors of SARS-CoV-2 RdRp. The structural insights and the optimized pharmacophoric model provided would augment the design of improved analogs of Remdesivir that could expand treatment options for COVID-19. The next study sought to explore the therapeutic targeting of human rhinoviruses (HRV) amidst challenges associated with the existence of a wide variety of HRV serotypes. By employing advanced computational techniques, the molecular mechanism of inhibition of a novel benzothiophene derivative that reportedly binds HRV-B14 was investigated. An analysis of the residue-residue interaction profile revealed of HRV upon the benzothiophene derivative binding revealed a distortion of the hitherto compacted and extensively networked HRV structure. This was evidenced by the fewer inter-residue hydrogen bonds, reduced van der Waals interactions, and increased residue flexibility. However, a decrease in the north-south wall's flexibility around the canyon region also suggested that the benzothiophene derivative's binding impedes the “breathing motion” of HRV-B14; hence its inhibition. The next study in the first domain of the research investigated the structural and molecular mechanisms of action associated with the dual inhibitory activity of IMP-1088. This novel compound reportedly inhibits human N-myristoyltransferase subtypes 1 and 2 towards common cold therapy. This is because it has emerged that the pharmacological inhibition of Nmyristoyltransferase is an efficient non-cytotoxic strategy to completely thwart the replication process of rhinovirus toward common cold treatment. Using augmentative computational and nanosecond-based analyses, findings of the study revealed that the steady and consistent interactions of IMP-1088 with specific residues; Tyr296, Phe190, Tyr420, Leu453, Gln496, Val181, Leu474, Glu182, and Asn246, shared within the binding pockets of both HNMT subtypes, in addition to peculiar structural changes account for its dual inhibitory potency. Findings thus unveiled atomistic and structural perspectives that could form the basis for designing novel dualacting inhibitors of N-myristoyltransferase towards common cold therapy. In the second domain of the research, the mechanism of action of some small molecule inhibitors against DHFR, FDTS, and Mtb ATP synthase in treating tuberculosis is extensively investigated and reportedly subsequently. To begin with, the dual therapeutic targeting of crucial enzymes in the folate biosynthetic pathway was explored towards developing novel treatment methods for TB. Therefore, the study investigated the molecular mechanisms and structural dynamics associated with dual inhibitory activity of PAS-M against both DHFR and FDTS, which hitherto was unclear. MD simulations revealed that PAS-M binding towards DHFR and FDTS is characterized by a recurrence of strong conventional hydrogen bond interactions between a peculiar site residue the 2-aminov decahydropteridin-4-ol group of PAS-M. Structural dynamics of the bound complexes of both enzymes revealed that, upon binding, PAS-M is anchored at the entrance of hydrophobic pockets by a strong hydrogen bond interaction while the rest of the structure gains access to deeper hydrophobic residues to engage in favorable interactions. Further analysis of atomistic changes of both enzymes showed increased C-α atom deviations and an increase C-α atoms radius of gyration consistent with structural disorientations. These conformational changes possibly interfered with the enzymes' biological functions and hence their inhibition as experimentally reported. Additionally, in this domain, the therapeutic targeting of the ATP machinery of Mtb by Bedaquiline (BDQ) was explored towards unravelling the structures and atomistic perspectives that account for the ability of BDQ to selectively inhibits mycobacterial F1Fo-ATP synthase via its rotor c-ring. BDQ is shown to form strong interaction with Glu65B and Asp32B and, consequently, block these residues' role in proton binding and ion. BDQ binding was also revealed to impede the rotatory motion of the rotor c-ring by inducing a compact conformation on the ring with its bulky structure. Complementary binding of two molecules of BDQ to the rotor c-ring, proving that increasing the number of BDQ molecule enhances inhibitory potency. The last study in this research domain investigated the impact of triple mutations (L59V, E61D, and I66M) on the binding of BDQ to Mtb F1F0 ATP-synthase. The study showed that the mutations significantly impacted the binding affinity of BDQ, evidenced by a decrease in the estimated binding free energy (ΔG). Likewise, the structural integrity and conformational architecture of F1F0 ATP-synthase was distorted due to the mutation, which could have interfered with the binding of BDQ. The third domain of the research in this thesis investigated some small molecule inhibitors' inhibitory mechanism against some therapeutic targets in cancer, specifically PTPRZ and hTCRvi CD1d. Studies in the third domain of the research in the thesis began with the investigation of the investigation of the inhibitory mechanism of NAZ2329, an allosteric inhibitor of PTPRZ, by specifical investigating its binding effect on the atomic flexibility of the WPD-loop. Having been established as crucial determinant of the catalytic activity of PTPRZ an implicated protein in glioblastoma cells, its successfully therapeutic modulation could present a viable treatment option in glioblastoma. Structural insights from an MD simulation revealed that NAZ2329 binding induces an open conformation of the WPD-loop which subsequently prevents the participation of the catalytic aspartate of PTPRZ from participating in catalysis hence inhibiting the activity of PTPRZ. A pharmacophore was also created based of high energy contributing residues which highlighted essential moieties of NAZ2329 and could be used in screening compound libraries for potential inhibitors of PTPRZ. A second study in this domain sought to explore how structural modification could improve a therapeutic agent's potency from an atomistic perspective. This study was based on an earlier report in which the incorporation of a hydrocinnamoyl ester on C6’’ and C4-OH truncation of the sphingoid base of KRN7000 generated a novel compound AH10-7 high therapeutic potency and selectivity in human TCR-CD1d and subsequently results in the activation of invariant natural killer T cells (iNKT). The hydrocinnamoyl ester moiety was shown to engage in high-affinity interactions, possibly accounting for the selectivity and higher potency of AH10-7. Molecular and structural perspectives provided could aid in the design of novel α-GalCer derivatives for cancer immunotherapeutics. Chapter 3 provides theoretical insights into the various molecular modeling tools and techniques employed to investigate the various conformational changes, structural conformations, and the associated mechanism of inhibitions of the studied inhibitors towards viral, tuberculosis, and cancer therapy. Chapter 4 provided sufficient details on druggability and drug-likeness principles and their recent advancements in the drug discovery field. The study also presents the different computational tools and their reliability of predictive analysis in the drug discovery domain. It thus provides a comprehensive guide for computational-oriented drug discovery research. Chapter 5 provides an understanding of the binding mechanism of Remdesivir, providing structural and conformational implications on SARS-CoV-2 RdRp upon its binding and identifying its crucial pharmacophoric moieties. Chapter 6 explains the mechanism of inhibition of a novel benzothiophene derivative, revealing its distortion of the native extensively networked and compact residue profile. Chapter 7 unravels molecular and structural bases behind this dual inhibitory potential of the novel inhibitor IMP-1088 toward common cold therapy using augmentative computational and cheminformatics methods. The study also highlights the pharmacological propensities of IMP- 1088. Chapter 8 unravels the molecular mechanisms and structural dynamics of the dual inhibitory activity of PAS-M towards DHFR and FDTS. Chapter 9 reports the structural dynamics and atomistic perspectives that account for the reported ability of BDQ to halt the ion shuttling ability of mycobacterial c-ring. Chapter 10 presents the structural dynamics and conformational changes that occur on Mtb F1F0 ATP-synthase binding as a result of the triple mutations using molecular dynamics simulations, free energy binding, and residue interaction network (RIN) analyses. Chapter 11 explored the impact of NAZ2329, a recently identified allosteric inhibitor of Protein Tyrosine Phosphatase Receptor Zeta (PTPRZ), on the atomic flexibility of the WPD-loop, an essential loop in the inhibition of PTPRZ. The study also presents the drug-likeness of NAZ2329 using in silico techniques and its general inhibitory mechanism. Chapter 12 provides atomistic insights into the structural dynamics and selective mechanisms of AH10-7 for human TCR-CD1d towards activating iNKT cells. The studies in this thesis collectively present a thorough and comprehensive in silico perspective that characterizes the pharmacological inhibition of some known therapeutic targets in viral infections, tuberculosis, and cancer. The augmentative integration of computational methods to provide structural insights could help design highly selective inhibitors of these therapeutic targets. Therefore, the findings presented are fundamental to the design and development of next generation lead compounds with improved therapeutic activities and minimal toxicities

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD