Molecular modelling and drug design

Antiproliferative, apoptosis-inducing activity and molecular docking studies of sydnones compounds

Objective

To evaluate the antiproliferative and apoptosis inducing activity of different sydnones on cancer cell lines and their interaction with cancer proteins by molecular docking studies.

Material and Methods

Antiproliferative activity was carried out by MTT assay and apoptosis inducing activity was performed by DAPI and Annexin V and propidium iodide staining. Molecular docking studies were performed using AutoDock Tools 1.5.6. Pharmacokinetics properties like ADME and toxicity were analysed by pkCSM web server.

Result

In this study, four new sydnone compounds 3-(4-nonylbiphenyl-4'-yl) sydnone (MC-182), 3-(4-propylbiphenyl-4'-yl) sydnone (MC-454), 3-(4-hexylbiphenyl-4'-yl) sydnone (MC-433), and 3-(4-methylbiphenyl-4'-yl) sydnone (MC-431) were screened for antiproliferative and apoptotic effect against BT-474 (human breast cancer), HeLa (human cervical cancer) and Jurkat (human myeloid leukemia) Mostly, all the sydnone compounds exhibited decent antiproliferative effectiveness, but compound MC-431, MC-433, and MC-454 showed more antiproliferative activity (IC50 1.71, 10.09 and 2.87 μM against BT-474, Hela and Jurkat cell line, respectively). The changes of morphological characteristics of cancer cells determined by staining techniques indicate the apoptotic cell death. The molecular docking and interaction studies were carried out between sydnones with cancer proteins (epidermal growth factor domain receptor tyrosine kinase [EGF-TK], tumor necrosis factor-alpha [TNF-α] and Caspase3. Among all four sydnone molecules, two compounds MC-454 and MC-431 showed good binding energy with targeted proteins. Drug-like property was predicted by ADME toxicity study.

Conclusion

The results indicate sydnone compounds were found to exhibit anticancer activity by inducing apoptosis. The molecular docking study of sydnones with cancer proteins showed a decent interaction affinity. The results of absorption, distribution, metabolism, excretion and toxicity studies by the Insilco approach also proved that MC-454 sydnone showed better In-Vivo administration. Thus, the current research work indicates that these sydnone compounds would be prospective in developing anticancer medicines.

Computational Drug Designing and Prediction Of Important Parameters Using in silico Methods- A Review

BACKGROUND

Computational or in silico studies are undertaken to assess the drug like properties of lead compounds. These studies help in fast prediction of relevant properties.

OBJECTIVE

Through this review, an effort is made to encapsulate some of the important parameters which should be met by a compound for it to be considered as a potential drug candidate along with an overview of automated softwares which can be used for making various predictions.

METHODS

Drug uptake, its absorption, evacuation and associated hazardous effects are important factors for consideration in drug designing and should be known in early stages of drug development. Several important physicochemical properties like molecular weight, polar surface area (PSA), molecular flexibility etc. have to be taken into consideration in drug designing. Toxicological assessment is another important aspect of drug discovery which predicts the safety and adverse effects of a drug.

RESULTS

Additionally, bioactivity scores of probable drug leads against various human receptors can also be predicted to evaluate the probability of them to act as a potential drug candidate. The in vivo biological targets of a molecule can also be efficiently predicted by molecular docking studies.

CONCLUSION

Some important software like iGEMDOCK, AutoDock, OSIRIS property explorer, Molinspiration, MetaPrint2D, admetSAR and their working methodology and principle of working have been summarized in this review.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.

Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin?

Innovative biologics, including cell therapeutics, virus-like particles, exosomes,recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the maintherapeutic entities in manufacturing over the next decades. This molecular variety causes agrowing need for a general change of methods as well as mindset in the process development stage,as there are no platform processes available such as those for monoclonal antibodies. Moreover,market competitiveness demands hyper-intensified processes, including accelerated decisionstoward batch or continuous operation of dedicated modular plant concepts. This indicates gaps inprocess comprehension, when operation windows need to be run at the edges of optimization. Inthis editorial, the authors review and assess potential methods and begin discussing possiblesolutions throughout the workflow, from process development through piloting to manufacturingoperation from their point of view and experience. Especially, the state-of-the-art for modeling inred biotechnology is assessed, clarifying differences and applications of statistical, rigorousphysical-chemical based models as well as cost modeling. “Digital-twins” are described and effortsvs. benefits for new applications exemplified, including the regulation-demanded QbD (quality bydesign) and PAT (process analytical technology) approaches towards digitalization or industry 4.0based on advanced process control strategies. Finally, an analysis of the obstacles and possiblesolutions for any successful and efficient industrialization of innovative methods from processdevelopment, through piloting to manufacturing, results in some recommendations. A centralquestion therefore requires attention: Considering that QbD and PAT have been required byauthorities since 2004, can any biologic manufacturing process be approved by the regulatoryagencies without being modeled by a “digital-twin” as part of the filing documentation?

Heterocyclic Anticancer Compounds: Recent Advances and the Paradigm Shift towards the Use of Nanomedicine's Tool Box

The majority of heterocycle compounds and typically common heterocycle fragments present in most pharmaceuticals currently marketed, alongside with their intrinsic versatility and unique physicochemical properties, have poised them as true cornerstones of medicinal chemistry. Apart from the already marketed drugs, there are many other being investigated for their promising activity against several malignancies. In particular, anticancer research has been capitalizing on the intrinsic versatility and dynamic core scaffold of these compounds. Nevertheless, as for any other promising anticancer drugs, heterocyclic compounds do not come without shortcomings. In this review, we provide for a concise overview of heterocyclic active compounds and families and their main applications in medicine. We shall focus on those suitable for cancer therapy while simultaneously addressing main biochemical modes of action, biological targets, structure-activity relationships as well as intrinsic limitation issues in the use of these compounds. Finally, considering the advent of nanotechnology for effective selective targeting of drugs, we shall discuss fundamental aspects and considerations on nanovectorization of such compounds that may improve pharmacokinetic/pharmacodynamic properties of heterocycles.

Accessible haptic technology for drug design applications

Structure-based drug design is a creative process that displays several features that make it closer to human reasoning than to machine automation. However, very often the user intervention is limited to the preparation of the input and analysis of the output of a computer simulation. In some cases, allowing human intervention directly in the process could improve the quality of the results by applying the researcher intuition directly into the simulation. Haptic technology has been previously explored as a useful method to interact with a chemical system. However, the need of expensive hardware and the lack of accessible software have limited the use of this technology to date. Here we are reporting the implementation of a haptic-based molecular mechanics environment aimed for interactive drug design and ligand optimization, using an easily accessible software/hardware combination.

Mining the National Cancer Institute's tumor-screening database: identification of compounds with similar cellular activities

In an effort to enhance access to information available in the National Cancer Institute's (NCI) anticancer drug-screening database, a new suite of Internet accessible (http://spheroid. ncifcrf.gov) computational tools has been assembled for self-organizing map-based (SOM) cluster analysis and data visualization. A range of analysis questions were initially addressed to evaluate improvements in SOM cluster quality based on the data-conditioning procedures of Z-score normalization, capping, and treatment of missing data as well as completeness of drug cell-screening data. These studies established a foundation for SOM cluster analysis of the complete set of NCI's publicly available antitumor drug-screening data. This analysis identified relationships between chemotypes of screened agents and their effect on four major classes of cellular activities: mitosis, nucleic acid synthesis, membrane transport and integrity, and phosphatase- and kinase-mediated cell cycle regulation. Validations of these cellular activities, obtained from literature sources, found (i) strong evidence supporting within cluster memberships and shared cellular activity, (ii) indications of compound selectivity between various types of cellular activity, and (iii) strengths and weaknesses of the NCI's antitumor drug screen data for assigning compounds to these classes of cellular activity. Subsequent analyses of averaged responses within these tumor panel types find a strong dependence on chemotype for coherence among cellular response patterns. The advantages of a global analysis of the complete screening data set are discussed.