Software based approaches for drug designing and development: A systematic review on commonly used software and its applications

Recent Advances in Nanocarrier-based Approaches to Atopic Dermatitis and Emerging Trends in Drug Development and Design

Atopic dermatitis (AD), commonly known as Eczema, is a non-communicable skin condition that tends to become chronic. The deteriorating immunological abnormalities are marked by mild to severe erythema, severe itching, and recurrent eczematous lesions. Different pharmacological approaches are used to treat AD. The problem with commercial topical preparations lies in the limitation of skin atrophy, systemic side effects, and burning sensation that decreases patient compliance. The carrier-based system promises to eliminate these shortcomings; thus, a novel approach to treating AD is required. Liposomes, microemulsions, solid lipid nanoparticles (SLNs), nanoemulsions, etc., have been developed recently to address this ailment. Despite extensive research in the development method and various techniques, it has been challenging to demonstrate the commercial feasibility of these carrier- based systems, which illustrates a gap among the different research areas. Further, different soft wares and other tools have proliferated among biochemists as part of a cooperative approach to drug discovery. It is crucial in designing, developing, and analyzing processes in the pharmaceutical industry and is widely used to reduce costs, accelerate the development of biologically innovative active ingredients, and shorten the development time. This review sheds light on the compilation of extensive efforts to combat this disease, the product development processes, commercial products along with patents in this regard, numerous options for each step of computer-aided drug design, including in silico pharmacokinetics, pharmacodynamics, and toxicity screening or predictions that are important in finding the drug-like compounds.

Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery

The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it possible to provide cost-effective, robust, secure, and scalable alternatives to the on-premise (on-prem) HPC via Cloud, Fog, and Edge computing. It has enabled recent state-of-the-art machine learning (ML) and artificial intelligence (AI)-based tools for drug discovery, such as BERT, BARD, AlphaFold2, and GPT. This chapter attempts to overview types of software architectures for developing scientific software or application with deployment agnostic (on-prem to cloud and hybrid) use cases. Furthermore, the chapter aims to outline how the innovation is disrupting the orthodox mindset of monolithic software running on on-prem HPC and provide the paradigm shift landscape to microservices driven application programming (API) and message parsing interface (MPI)-based scientific computing across the distributed, high-available infrastructure. This is coupled with agile DevOps, and good coding practices, low code and no-code application development frameworks for cost-efficient, secure, automated, and robust scientific application life cycle management.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

QSAR, homology modeling, and docking simulation on SARS-CoV-2 and pseudomonas aeruginosa inhibitors, ADMET, and molecular dynamic simulations to find a possible oral lead candidate

Background

In seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives.

Results

The QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q² _LOO = 0.8027; R ² = 0.8735; R ² _ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation-lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run.

Conclusion

Our acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00362-z.

Characterization of Umami Dry-Cured Ham-Derived Dipeptide Interaction with Metabotropic Glutamate Receptor (mGluR) by Molecular Docking Simulation

Dry-cured ham-derived dipeptides, generated along a dry-curing process, are of high importance since they play a role in flavor development of dry-cured ham. The objective of this study was to analyze the residues of the less-studied metabotropic glutamate receptor 1 (mGluR1) implicated in the recognition of umami dry-cured ham dipeptides by molecular docking simulation using the AutoDock Suite tool. AH, DA, DG, EE, ES, EV, and VG (and glutamate) were found to attach the enzyme with inhibition constants ranging from 12.32 µM (AH) to 875.75 µM (ES) in the case if Rattus norvegicus mGluR1 and 17.44 µM (VG) to 294.68 µM (DG) in the case of Homo sapiens, in the open–open conformations. Main interactions were done with key receptor residues Tyr74, Ser186, Glu292, and Lys409; and Ser165, Ser186, and Asp318, respectively, for the two receptors in the open–open conformations. However, more residues may be involved in the complex stabilization. Specifically, AH, EE and ES relatively established a higher number of H-bonds, but AH, EV, and VG presented relatively lower Ki values in all cases. The results obtained here could provide information about structure and taste relationships and constitute a theoretical reference for the interactions of novel umami food-derived peptides.

Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development

In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.

Applications of QbD-based Software’s in Analytical Research and Development

Background:

Quality by design-based software’s in analytical research and development normally encompasses multiple objectives. For decades, this task has been attempted through trial and error, supplemented with the previous experience, knowledge, and wisdom of analytical researchers.

Objective:

The study analyzes the current QbD-assisted software’s, such as design-experts, minitab, fusion product development, etc., and its broad implementations in an analytical research and development.

Methods:

The traditional approach may fails to meet the intended purpose by trial and error procedure during analytical research and development. However, modern scientific technology is equipped with highly advanced features associated with the software of the QbD paradigm. The impact and interactions between the critical process variables and critical method attributes such as resolution, tailing, etc. can be well understood by the screening, optimization, and robustness studies based on the principles of experimental design.

Results:

The design of experiments assimilate statistical multi-variate analysis instead of one factor at a time approach. This also provides a prominent, most reliable quality output, which is also essential for getting highly robust method as well as to obtain homogenous product development.

Conclusion:

The present review, critically discussed about the various QbD based multivariate software and their applications in drug development and analytical research.

Synthesis, characterization, molecular docking and anticancer studies of fluoroaniline derivatives of hydroxybenzoquinone and hydroxynaphthoquinone

Two series of fluoro substituted-anilino derivatives of naturally occurring hydroxybenzoquinone and hydroxynaphthoquinone were synthesized using TFA as catalyst to improve the product yield. Recently, fluorine containing compounds are being used as anticancer drugs. The aim of this study is to find compounds that are active against melanoma cells. This six new fluoro substituted quinone compounds were synthesized and characterized. All of these compounds were then subjected to molecular docking studies against B-raf protein using Discovery Studio 4.0 and the binding affinities were calculated. The energy scores of in silico analysis revealed that all the compounds exhibited better binding affinity towards B-raf protein. Moreover, all the derivatives and the parent compounds, embelin and plumbagin along with standard drug, PLX4032 were investigated for its in vitro cytotoxicity in A375 cell lines (Melanoma) and in vitro ELISA assay in B-raf isolated from melanoma cells. Among them, 5-(3-chloro-4-trifluoromethoxy-phenylamino)-2-hydroxy-3-undecyl-[1,4]benzoquinone exhibited lower cell viability with lowest LC₅₀ of 12.25 μg/mL and thus poses suitability to be a lead molecule for further drug discovery.Communicated by Ramaswamy H. Sarma.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.