Identification of Benzoxazolinone Derivatives Based Inhibitors for Depression and Pain Related Disorders Using Human Serotonin and Norepinephrine Transporter as Dual Therapeutic Target: A Computational Approach

Pain is commonly associated with depression. Both pain and depression share common biological pathways and neurotransmitters, which has implications for the treatment of both disorders. A drug that could ameliorate both pain and depression could be beneficial in the development of new therapeutics in the management of disorders associated with pain/depression dyad. Alterations in the neurotransmitters namely, serotonin and norepinephrine in the central nervous system (CNS) have been implicated in the pathophysiology of pain and depression. Serotonin and norepinephrine reuptake inhibitors (SNRIs) have been implicated as a novel therapeutic target for a wide range of biological functions, including pain, anxiety and depression. 2-benzoxazolinone (2-BOA) from the mangrove Acanthus ilicifolius and its derivatives have been reported for its analgesic and antidepressant activities. In the present work, docking studies were done on the crystal structure of human transporters of serotonin (hSERT) and on homology modeled human transporters of norepinephrine (hNET) as therapeutic targets of depression and pain related disorders using 2-BOA and its derivatives as potential candidates. A homology model for hNET was constructed using MODELLER and validated. Further docking studies were done on hSERT and hNET using 2-BOA and its structural analogs. The result of the study proposes the possible potential candidate among 2-BOA derivatives that may be further developed as a therapeutic lead compound for use in disorders associated with depression and pain

Structure Based Ligand Design for Monoamine Transporters and Mitogen Activated Kinase 5

Depression is a major psychological disorder that affects a person\u27s mental and physical abilities. The National Institute of Mental Health (NIMH) classified it as a serious medical illness. It causes huge economic, as well as financial impact on the people, and it is also becoming a major public health issue. Antidepressant drugs are prescribed to mitigate the suffering caused by this disorder. Different generations of antidepressants have been developed with dissimilar mechanisms of action. According to the Center for Disease Control, the usage of antidepressants has skyrocketed by 400 percent increase over 2005- 2008 survey period. This dramatic rise in usage indicates that these are the most prescribed drugs in the US. Even with the FDA mandated black box warning of increased suicidal thoughts upon use of selected antidepressants, these drugs are still being used at a higher rate. All classes of antidepressants are plagued by side effects with mainly sexual dysfunction common among them. To avoid the adverse effects, an emphasis is to discover novel structural drug scaffolds that can be further developed as a new generation of antidepressants. The importance of this research is to discover structurally novel antidepressants by performing in silico virtual screening (VS) of chemical databases using the serotonin transporter (SERT). In the absence of a SERT crystal structure, a homology model was developed. The homology model was utilized to develop the first structure-based pharmacophore for the extracellular facing secondary ligand binding pocket. The pharmacophore captured the necessary drug-SERT interaction pattern for SERT inhibitory action. This pharmacophore was employed as one of the filters for VS of candidate ligands. The ten compounds identified were purchased and tested pharmacologically. Out of the ten hits, three structurally novel ligands were identified as lead compounds. Two of these compounds exhibited selectivity towards SERT; the remaining lead compound was selective towards the dopamine transporter and displayed cocaine inhibition. The two SERT selective compounds will provide new opportunities in the development of novel therapeutics to treat depression. For dopamine transporter (DAT), the study was based on recently developed structurally diverse photo probes. In an effort to better understand the binding profile similarities among these different scaffolds, the photo probes were docked into DAT. The finger print analysis of the interaction pattern of docked poses was performed to identify the inhibitor-binding sites. For mitogen activated protein kinase 5 (MEK5), given the lack of structural information, a homology model of MEK5 was developed to guide the rational design of inhibitors. Docking of known MEK5 inhibitors into the homology model was performed to understand the inhibitory interaction profile. Several series of analogues were designed utilizing the generated interaction profile

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data

Medicinal Chemistry Updates on Bacterial Efflux Pump Modulators

Antibiotic resistance is one of the most pressing health issues of our days. It can arise due to a multiplicity of factors, such as target modification, decrease in the drug uptake, changes in the metabolic pathways and activation of efflux pumps. The overexpression of efflux pumps is responsible for the extrusion of drugs, making antibiotic therapy fail, as the quantity of intracellular antibiotic is not enough to provide the desired therapeutic effect. Efflux pumps can be included in five families according to their composition, nature of substrates, energy source, and number of transmembrane spanning regions. The ABC superfamily is mainly found in Gram-positive bacteria, use ATP as an energy source, and only a limited number of ABC pumps confer multidrug resistance (MDR). On the other hand, the MFS family, most present in Gram-positive bacteria, and the RND family, characteristic of Gram-negative bacteria, are most associated with antibiotic resistance. A wide variety of inhibitors have been disclosed for both families, from either natural or synthetic sources, or even drugs that are currently in therapy for other diseases. The other two families are the SMR, which are the smallest drug efflux proteins known, and the MATE family, whose pumps can also resort to the sodium gradient as an energy source. In this review, it is intended to present a comprehensive review of the classes of efflux pump inhibitors from the various sources, highlighting their structure-activity relationships, which can be useful for medicinal chemists in the pursuit of novel efflux pump inhibitors. Copyright© Bentham Science Publishers; For any queries, please email at [email protected].[Not available

Design, synthesis and optimization of bioactive compounds: a medicinal chemistry approach

Il lungo processo di scoperta di un nuovo farmaco coinvolge diverse fasi e richiede l'integrazione di diverse discipline scientifiche e metodologie. La chimica farmaceutica, che comprende discipline come la chimica analitica, organica e computazionale, gioca un ruolo cruciale nella fase iniziale del processo di drug discovery ed è essenziale nello sviluppo di molecole con potenziale attività biologica nonché nella comprensione dei meccanismi delle malattie o delle strutture dei bersagli macromolecolari. Il mio percorso di dottorato è stato caratterizzato e fondamentale per accrescere la comprensione e sviluppare l'utilizzo di diverse tecniche, tra cui: chimica analitica, cromatografia, spettroscopia, diffrazione a raggi X, microscopia ed altre ancora; mi sono inoltre dedicata all'ottimizzazione della sintesi organica per ottenere migliori rese con reazioni più green ed economiche. In questi anni ho trattato con maggiore enfasi le seguenti tecniche: spettrometria di massa (MS), risonanza magnetica nucleare (NMR), tecniche computazionali e dicroismo circolare (CD); la mia tesi, dopo un'introduzione alla teoria e alle possibili applicazioni delle suddette, illustra come siano state utilizzate sinergicamente in diversi percorsi di ricerca. Nel progetto principale (Sezione 2), l'uso congiunto di queste tecniche nello studio delle interazioni di piccole molecole con il G-quadruplex (G4), ha evidenziato il loro potenziale nelle applicazioni antitumorali, antinfiammatorie, antivirali e neuroprotettive. Le metodologie fondamentali includono l'ESI-MS per uno screening rapido e per una prima valutazione dell’efficienza dell'interazione, l'NMR per la risoluzione tridimensionale della struttura e per approfondire la natura del legame ligando:target, il CD per convalidare i risultati e valutare la topologia del G4 e le tecniche computazionali per prevedere le interazioni presenti e per perfezionare le strutture predette. Sono stati esaminati scaffold diversi dimostrando il loro potenziale nell’interazione con diversi folding degli acidi nucleici e, in particolare con le strutture G4, suggerendo i loro possibili effetti antiproliferativi grazie all'inibizione dell'attività telomerasica mediante la stabilizzazione del G4. I due progetti secondari presentati nella Sezione 3 mostrano, rispettivamente, uno lo sviluppo di composti contenenti selenio per la mitigazione della neurodegenerazione e l'altro l'identificazione di inibitori di fosfodiesterasi (PDE) per le malattie neurodegenerative; questi progetti hanno dimostrato l'efficacia dell'integrazione delle tecniche computazionali con saggi sperimentali nella scoperta di nuovi farmaci. Il primo progetto illustra la preparazione di derivati della selenofluoxetina, enfatizzandone la capacità di mitigare lo stress ossidativo e offrire benefici neuroprotettivi. L'indagine ha coinvolto lo studio delle reazioni di questi composti con le specie reattive dell'ossigeno (ROS), sia con metodi sperimentali che computazionali, fornendo informazioni cruciali sui loro meccanismi. Il secondo progetto coinvolge l'uso di tecniche computazionali per la progettazione di nuovi inibitori di PDE utilizzando il docking molecolare e le simulazioni di dinamica molecolare (MD). I composti sono stati sottoposti ad uno screening, individuando potenziali candidati farmaci in grado di formare le interazioni significative con gli amminoacidi cruciali per il legame con le PDE. Infine le simulazioni di MD hanno ulteriormente convalidato l’effettiva stabilità dei complessi formati consolidando i risultati e i test in vitro hanno dimostrato un'elevata attività inibitoria contro la PDE9, paragonabile a quella di un noto inibitore. La sintesi e l'ottimizzazione di potenziali candidati farmaci, insieme ad una comprensione esaustiva delle interazioni bersaglio:ligando mediante l’uso di diverse tecniche strumentali, costituiscono la chiave del successo nella scoperta di nuovi farmaci.The extensive process of drug discovery encompasses multiple stages and requires a convergence of diverse scientific disciplines and methodologies. Medicinal chemistry, which includes sciences like analytical, organic, and computational chemistry, plays a significant role in the preliminary stage of drug discovery and is pivotal in developing potentially bioactive molecules and unravelling disease mechanisms or macromolecular target structures. My PhD has been characterized and essential in enhancing the understanding and development of the use of various techniques, including analytical chemistry, chromatography, spectroscopy, X-ray diffraction, microscopy, among others. Furthermore, I have dedicated myself to optimizing organic synthesis to achieve better yields through greener and more cost-effective reactions. Throughout these years, I have focused with greater emphasis on the following techniques: mass spectrometry (MS), nuclear magnetic resonance (NMR), computational techniques, and circular dichroism (CD). My thesis, after an introduction to the theory and possible applications of these techniques, illustrates how they have been synergistically employed in various research paths. In the primary project (Section 2), their collective use in studying small molecules' interactions with G-quadruplex (G4) highlighted their potential in anti-cancer, anti-inflammatory, anti-viral, and neuroprotective applications. Essential methodologies include ESI-MS for rapid screening and understanding interaction efficiency, NMR for 3D structure resolution and binding insights, CD spectroscopy to validate findings and assess G4 topology, and computational tools for predicting interactions and refining structures. Different scaffolds were investigated, revealing their potential in targeting DNA arrangements, particularly G4 structures, suggesting their anti-proliferative effects by inhibiting telomerase activity through G4 stabilization. Two ancillary projects (Section 3) showcased, respectively, one the development of seleno-containing compounds for neurodegeneration mitigation and the other the identification of inhibitors of phosphodiesterase (PDE) for neurodegenerative disorders; these projects demonstrated the efficiency of integrating computational techniques with experimental assays to streamline drug discovery. The first project presented focused on the preparation of selenofluoxetine derivatives, emphasizing their capacity to mitigate oxidative stress and offer neuroprotective effects. The investigation delved into understanding the compounds' reactions with reactive oxygen species (ROS), both in experimental and computational settings, providing crucial insights into their mechanisms. The second project showed the use of computational tools for designing novel PDE inhibitors through molecular docking, and MD simulations. After a meticulous validation of the docking method, the compounds generated through combinatorial chemistry and the molecules of an internal database underwent a screening, leading to the identification of potential hits displaying significant interactions with crucial amino acids for PDE binding. These findings allowed a preliminary structure-activity relationship (SAR) study and identified crucial design features for potential PDE inhibitors. Eventually, MD simulations further validated the stability of the formed complexes, consolidating the findings and in vitro testing demonstrated high inhibitory activity against PDE9, comparable to a known inhibitor. The synthesis and optimization of potential drug candidates, when coupled with a comprehensive understanding of target:ligand interactions through diverse techniques, form the crux of successful drug discovery. The seamless integration of these methodologies continues to be paramount in creating novel pharmacological agents, driving the need for further research to refine and expedite the drug discovery process

The Effect of Data Curation on the Accuracy of Quantitative Structure-Activity Relationship Models

In the 33 years since the first public release of GenBank, and the 15 years since the publication of the first pilot assembly of the human genome, drug discovery has been awash in a tsunami of data. But it has only been within the past decade that medicinal chemists and chemical biologists have had access to the same sorts of large-scale, public-access databases as bioinformaticians and molecular biologists have had for so long. The release of this data has sparked a renewed interest in computational methods for rational drug design, but questions have arisen recently about the accuracy and quality of this data. The same question has arisen in other scientific disciplines, but it has a particular urgency to practitioners of Quantitative Structure-Activity Relationship (QSAR) modeling. By its nature QSAR modeling depends on both activity data and chemical structures. While activities are usually expressed as numerical scalar values, a form ubiquitous throughout the sciences, chemical structures (especially that must be interpretable as such by computer software) are stored in a variety of specialized formats which are much less common and mostly ignored outside of cheminformatics and related fields. While previous research has determined that a 5% error rate in data being used for modeling can cause a QSAR model to be non-predictive and useless for its intended purpose, and workflows have been proposed which reduce the effect of inconsistent chemical structure representations on model accuracy, a fundamental question remains: “how accurate are the structure and activity data freely available to researchers?” To this end, we have undertaken two surveys of data quality, one focusing on chemical structure information in Internet resources and a second examining the uncertainty associated with compounds reported in the medicinal chemistry literature as abstracted in ChEMBL. The results of these studies have informed the creation of an improved workflow for the curation of structure-activity data which is intended to identify problematic data points in raw data extracted from databases so that an expert human curator can examine the underlying literature and resolve discrepancies between reported values. This workflow was in turn applied to the creation of two QSAR models that were used to implement a virtual screen seeking molecules capable of binding to both the serotonergic reuptake transporter and the alpha2a adrenergic receptor. While no suitable compounds were identified in the initial screening process, regions of chemical space that may yield truly novel alpha 2a receptor ligands have been identified. These regions can be targeted in future efforts. Basing data curation workflows on manual processes by human curators is not particularly viable, as humans have a tendency to introduce errors by inattention even as they identify and repair other problems. Computers cannot effectively curate data either. While they are highly accurate when programmed properly, they lack human creativity and insight that would allow them to determine which data points represent truly inaccurate information. In order to effectively curate data, humans and computers must both be incorporated into a workflow that harnesses their strengths and limits their liabilities.Doctor of Philosoph

Stereoselective Synthesis of Highly Substituted Tetrahydropyrans through an Evans Aldol-Prins Strategy

A direct and general method for the synthesis of naturally occurring 2,3,4,5,6-pentasubstituted tetrahydropyrans has been developed, employing β,γ-unsaturated N-acyl oxazolidin-2-ones as key starting materials. The combination of the Evans aldol addition and the Prins cyclization allowed the diastereoselective and efficient generation of the desired oxacycles in two fashions: a one-pot Evans aldol–Prins protocol, in which five new σ bonds and five contiguous stereocenters were straightforwardly generated, and a two-step version, which additionally permitted the isolation of β,γ-unsaturated alcohol precursors bearing an N-acyl oxazolidin-2-one in the α position. From these alcohols were also obtained halogenated pentasubstituted tetrahydropyrans as well as 2,3,4,5-tetrasubstituted tetrahydrofurans, shedding light on the mechanism of the process. Computational studies were consistent with the experimental findings, and this innovative Evans aldol–Prins strategy was performed for the preparation of a battery of more than 30 densely substituted tetrahydropyrans, unprecedentedly fused to a 1,3-oxazinane-2,4-dione ring, both in a racemic fashion and in an enantiomeric fashion. These novel molecules were successfully submitted to several transformations to permit simple access to a variety of differently functionalized tetrahydropyrans. Most of these unique molecules were evaluated for their antimicrobial activity against Gram-positive and Gram-negative bacteria and the yeast Candida albicans, and some structure–activity relationships were established

QSAR model development for early stage screening of monoclonal antibody therapeutics to facilitate rapid developability

PhD ThesisMonoclonal antibodies (mAbs) and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. With nearly 80% of drugs failing in clinical development mainly due to lack of efficacy and safety there arises an urgent need for better understanding of biological activity, affinity, pharmacology, toxicity, immunogenicity etc. thus leading to early prediction of success/failure. In this study a hybrid modelling framework was developed that enabled early stage screening of mAbs. The applicability of the experimental methods was first tested on chemical compounds to assess the assay quality following which they were used to assess potential off target adverse effects of mAbs. Furthermore, hypersensitivity reactions were assessed using Skimune™, a non-artificial human skin explants based assay for safety and efficacy assessment of novel compounds and drugs, developed by Alcyomics Ltd. The suitability of Skimune™ for assessing the immune related adverse effects of aggregated mAbs was studied where aggregation was induced using a heat stress protocol. The aggregates were characterised by protein analysis techniques such as analytical ultra-centrifugation following which the immunogenicity tested using Skimune™ assay. Numerical features (descriptors) of mAbs were identified and generated using ProtDCal, EMBOSS Pepstat software as well as amino acid scales for different. Five independent and novel X block datasets consisting of these descriptors were generated based on the physicochemical, electronic, thermodynamic, electronic and topological properties of amino acids: Domain, Window, Substructure, Single Amino Acid, and Running Sum. This study describes the development of a hybrid QSAR based model with a structured workflow and clear evaluation metrics, with several optimisation steps, that could be beneficial for broader and more generic PLS modelling. Based on the results and observation from this study, it was demonstrated incremental improvement via selection of datasets and variables help in further optimisation of these hybrid models. Furthermore, using hypersensitivity and cross reactivity as responses and physicochemical characteristics of mAbs as descriptors, the QSAR models generated for different applicability domains allow for rapid early stage screening and developability. These models were validated with external test set comprising of proprietary compounds from industrial partners, thus paving way for enhanced developability that tackles manufacturing failures as well as attrition rates.European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie actions grant agreemen