Fundamental considerations in drug design

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood-brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure-activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.

Assessment of Antidiarrheal Effect of Oleuropein Through µ-Oopioid Receptor Interaction Pathway: In Vivo and in Silico Studies

Oleuropein (OLP), a compound predominantly found in olive leaves, has been known for its numerous biological activities, including antioxidant, anti-inflammatory, and antimicrobial properties. Despite its established therapeutic potential, its role in treating diarrhea has not been extensively explored. This study aimed to evaluate the antidiarrheal effects of OLP in an in vivo model and to investigate its molecular interactions using in silico docking studies, pharmacokinetic predictions, and toxicity analysis. In the in vivo study, castor oil was used to induce diarrhea in 3-day-old chicks, and the antidiarrheal effect of OLP was tested at doses of 10 and 20 mg/kg. The standard drug, loperamide (LOP) at 3 mg/kg, was used for comparison. The results showed that OLP at both doses significantly (p < 0.05) reduced diarrheal secretions and increased latency, with the 20 mg/kg dose demonstrating the most effective results. The combination of OLP (20 mg/kg) with LOP (3 mg/kg) further enhanced the antidiarrheal effect. In the in silico study, molecular docking revealed that both OLP and LOP exhibited strong binding affinities (BAs) to the key receptor, μ-opioid receptor associated with diarrhea, while OLP showed higher BA (‒8.9 kcal/mol) compared to LOP (‒8.7 kcal/mol). Pharmacokinetic analysis of OLP revealed favorable properties and toxicity studies revealed no acute toxicity, with an LD50 of 2000 mg/kg. In conclusion, the findings suggest that OLP possesses significant antidiarrheal potential both in vivo and through receptor interaction, positioning it as a promising natural therapeutic agent for managing diarrhea. Further studies are warranted to fully elucidate its mechanisms of action and clinical applicability.

In silico Profiling and Pharmacokinetic Modelling of Olivetol: Evaluating its Potential as a Therapeutic Agent for Diabetic Wound Healing

BACKGROUND

Diabetic wound healing poses a significant challenge due to the intricate disruptions in cellular and molecular processes induced by hyperglycaemia, leading to delayed or impaired tissue repair. Computational techniques offer a promising avenue for unravelling the complexities of diabetic wound healing by elucidating the molecular mechanisms involved.

METHODOLOGY

This study utilized in silico molecular docking and dynamics simulations to explore the potential therapeutic effectiveness of olivetol, a phenolic compound, in the context of diabetic wound healing. Furthermore, computational methodologies, encompassing pkCSM, Swiss ADME, OSIRIS® property explorer, PASS online web resource, and MOLINSPIRATION® software, were employed to forecast the pharmacokinetic properties, biological actions, and in vitro analyses, such as MTT and scratch assays, to evaluate the therapeutic effectiveness of olivetol in wound healing.

RESULTS AND DISCUSSION

Our findings have revealed olivetol to be a promising candidate for targeting multiple pathways implicated in diabetic wound healing. Its ability to modulate inflammation, oxidative stress, extracellular matrix remodeling, angiogenesis, and cell signaling suggests a multifaceted approach to promoting effective wound repair. Moreover, olivetol has been found to demonstrate strong binding affinity with key MRSA target proteins, indicating its potential as an antimicrobial agent against MRSA infections in diabetic wounds. The in vitro MTT assay demonstrated cell viability with an IC50 value of 40.80 μM, highlighting its cytotoxicity potential. Additionally, the scratch assay confirmed promising wound healing activity, showcasing its effectiveness in promoting cell migration and closure.

CONCLUSION

Olivetol emerges as a promising candidate for targeted interventions in non-healing diabetic wounds, particularly due to its ability to address prolonged inflammation, a common obstacle in diabetic wound healing.

Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

"Several birds with one stone": exploring the potential of AI methods for multi-target drug design

Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies have been adopted to cut costs and speed up the drug development process, serving as promising in silico approaches to efficiently design novel drug candidates targeting various health conditions. Most existing AI-driven drug discovery studies follow a single-target approach which focuses on identifying compounds that bind a target (i.e., one-drug-one-target approach). Polypharmacology is a relatively new concept that takes a systematic approach to search for a compound (or a combination of compounds) that can bind two or more carefully selected protein biomarkers simultaneously to synergistically treat the disease. Recent studies have demonstrated that multi-target drugs offer superior therapeutic potentials compared to single-target drugs. However, it is intuitively thought that searching for multi-target drugs is more challenging than finding single-target drugs. At present, it is unclear how AI approaches perform in designing multi-target drugs. In this paper, we comprehensively investigated the performance of multi-objective AI approaches for multi-target drug design. Our findings are quite counter-intuitive demonstrating that, in fact, AI approaches for multi-target drug design are able to efficiently generate more high-quality novel compounds than the single-target approaches while satisfying a number of constraints.

Exploring antiviral activity of Betanin and Glycine Betaine against dengue virus type-2 in transfected Hela cells

Dengue virus (DENV) infection is a worldwide public health concern infecting approximately 400 million individuals and about 40,000 mortalities yearly. Despite this, no licensed or readily available antiviral medication is currently available specifically for DENV infection, and therapy is typically symptomatic. Therefore, the objective of the study was to investigate the antiviral activity of Beta vulgaris L. phytoconstituents against DENV-2 targeting NS3 protein. The antiviral activity of phytochemicals was examined through virtual ligand-based screening, antiviral inhibition and dosage response assays, western blotting analysis and MD simulations. We conducted toxicological, and pharmacokinetic analysis to assess plant-based natural compound's efficacy, safety, and non-toxic doses. Molecular docking and MD simulation results revealed that the nonstructural protein-3 (NS3) might prove as a funamental target for Betanin and Glycine Betaine against Dengue virus. Betanin and Glycine betaine were initially studied for their non-toxic doses in HeLa, CHO, and Vero cells via MTT assay. HeLa cells were transiently transfected with cloned vector pcDNA3.1/Zeo(+)/DENV-2 NS3 along with non-toxic doses (80 μM-10 μM) of selected phytochemicals. The dose-response assay illustrated downregulated expression of DENV-2 NS3 gene after administration of Betanin (IC50 = 4.35 μM) and Glycine Betaine (IC50 = 4.49 μM). Dose response analysis of Betanin (80 μM-10 μM) depicted the significant inhibition of NS3 protein expression as well. These results suggested downregulated expression of DENV-2 NS3 at mRNA and protein level portraying the DENV replication inhibition. Based on our study findings, NS3 protease is depicted as distinctive DENV-2 inhibitor target. We will channel our study further into in vitro characterization employing the mechanistic study to understand the role of host factors in anti-flavi therapeutic.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Analyzing VEGFA/VEGFR1 Interaction: Application of the Resonant Recognition Model-Stockwell Transform Method to Explore Potential Therapeutics for Angiogenesis-Related Diseases

The interaction between vascular endothelial growth factor A (VEGFA) and VEGF receptor 1(VEGFR1) is a central focus for drug development in pathological angiogenesis, where aberrant angiogenesis underlies various anomalies necessitating therapeutic intervention. Identifying hotspots of these proteins is crucial for developing new therapeutics. Although machine learning techniques have succeeded significantly in prediction tasks, they struggle to pinpoint hotspots linked to angiogenic activity accurately. This study involves the collection of diverse VEGFA and VEGFR1 protein sequences from various species via the UniProt database. Electron-ion interaction Potential (EIIP) values were assigned to individual amino acids and transformed into frequency-domain representations using discrete Fast Fourier Transform (FFT). A consensus spectrum emerged by consolidating FFT data from multiple sequences, unveiling specific characteristic frequencies. Subsequently, the Stockwell Transform (ST) was employed to yield the hotspots. The Resonant Recognition Model (RRM) identified a characteristic frequency of 0.128007 with an associated wavelength of 1570 nm and RRM-ST identified hotspots for VEGFA (Human 36, 46, 48, 67, 71, 74, 82, 86, 89, 93) and VEGFR1 (Human 224, 259, 263, 290, 807, 841, 877, 881, 885, 892, 894, 909, 913, 1018, 1022, 1026, 1043). These findings were cross-validated by Hotspots Wizard 3.0 webserver and Protein Data Bank (PDB). The study proposes using a 1570 nm wavelength for photo bio modulation to boost VEGFA/VEGFR1 interaction in the condition that is needed. It also aims to reduce VEGFA/VEGFR2 interaction, limiting harmful angiogenesis in conditions like diabetic retinopathy. Also, the identified hotspots assist in designing agonistic or antagonistic peptides tailored to specific medical requirements with abnormal angiogenesis.

Therapeutic targeting of voltage-gated sodium channel NaV1.7 for cancer metastasis

This review focuses on the expression and function of voltage-gated sodium channel subtype NaV1.7 in various cancers and explores its impact on the metastasis driving cell functions such as proliferation, migration, and invasiveness. An overview of its structural characteristics, drug binding sites, inhibitors and their likely mechanisms of action are presented. Despite the lack of clarity on the precise mechanism by which NaV1.7 contributes to cancer progression and metastasis; many studies have suggested a connection between NaV1.7 and proteins involved in multiple signaling pathways such as PKA and EGF/EGFR-ERK1/2. Moreover, the functional activity of NaV1.7 appears to elevate the expression levels of MACC1 and NHE-1, which are controlled by p38 MAPK activity, HGF/c-MET signaling and c-Jun activity. This cascade potentially enhances the secretion of extracellular matrix proteases, such as MMPs which play critical roles in cell migration and invasion activities. Furthermore, the NaV1.7 activity may indirectly upregulate Rho GTPases Rac activity, which is critical for cytoskeleton reorganization, cell adhesion, and actin polymerization. The relationship between NaV1.7 and cancer progression has prompted researchers to investigate the therapeutic potential of targeting NaV1.7 using inhibitors. The positive outcome of such studies resulted in the discovery of several inhibitors with the ability to reduce cancer cell migration, invasion, and tumor growth underscoring the significance of NaV1.7 as a promising pharmacological target for attenuating cancer cell proliferation and metastasis. The research findings summarized in this review suggest that the regulation of NaV1.7 expression and function by small molecules and/or by genetic engineering is a viable approach to discover novel therapeutics for the prevention and treatment of metastasis of cancers with elevated NaV1.7 expression.

Insights into the Effects of Ligand Binding on Leucyl-tRNA Synthetase Inhibitors for Tuberculosis: In Silico Analysis and Isothermal Titration Calorimetry Validation

Incidences of drug-resistant tuberculosis have become common and are rising at an alarming rate. Aminoacyl t-RNA synthetase has been validated as a newer target against Mycobacterium tuberculosis. Leucyl t-RNA synthetase (LeuRS) is ubiquitously found in all organisms and regulates transcription, protein synthesis, mitochondrial RNA cleavage, and proofreading of matured t-RNA. Leucyl t-RNA synthetase promotes growth and development and is the key enzyme needed for biofilm formation in Mycobacterium. Inhibition of this enzyme could restrict the growth and development of the mycobacterial population. A database consisting of 2734 drug-like molecules was screened against leucyl t-RNA synthetase enzymes through virtual screening. Based on the docking scores and MMGBSA energy values, the top three compounds were selected for molecular dynamics simulation. The druggable nature of the top three hits was confirmed by predicting their pharmacokinetic parameters. The top three hits-compounds 1035 (ZINC000001543916), 1054 (ZINC000001554197), and 2077 (ZINC000008214483)-were evaluated for their binding affinity toward leucyl t-RNA synthetase by an isothermal titration calorimetry study. The inhibitory activity of these compounds was tested against antimycobacterial activity, biofilm formation, and LeuRS gene expression potential. Compound 1054 (Macimorelin) was found to be the most potent molecule, with better antimycobacterial activity, enzyme binding affinity, and significant inhibition of biofilm formation, as well as inhibition of the LeuRS gene expression. Compound 1054, the top hit compound, has the potential to be used as a lead to develop successful leucyl t-RNA synthetase inhibitors.

Structure-based drug design, molecular dynamics simulation, ADMET, and quantum chemical studies of some thiazolinones targeting influenza neuraminidase

The genetic mutability of the influenza virus leads to the existence of drug-resistant strains which is dangerous, particularly with the lingering coronavirus disease (COVID-19). This necessitated the need for the search and discovery of more potential anti-influenza agents to avert future outbreaks. In furtherance of our previous in-silico studies on 5-benzyl-4-thiazolinones as anti-influenza neuraminidase (NA) inhibitors, molecule 11 was selected as the template scaffold for the structure-based drug design due to its good binding, pharmacokinetic profiling, and better NA inhibitory activity. As such, eighteen (18) new molecules (11a-r) were designed with better MolDock scores as compared with the template scaffold and the zanamivir reference drug. However, the dynamic stability of molecule 11a in the binding cavity of the NA target (3TI5) showed water-mediated hydrogen and hydrophobic bondings with the active residues such as Arg118, Ile149, Arg152, Ile222, Trp403, and Ile427 after the MD simulation for 100 ns. The drug-likeness and ADMET assessment of all designed molecules predicted non-violation of the stipulated thresholds of Lipinski's rule and good pharmacokinetic properties respectively. In addition, the quantum chemical calculations also suggested the significant chemical reactivity of molecules with their smaller band energy gap, high electrophilicity, high softness, and low hardness. The results obtained in this study proposed a reliable in-silico viewpoint for anti-influenza drug discovery and development.Communicated by Ramaswamy H. Sarma.