Structure-based molecular modeling in SAR analysis and lead optimization

Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments

This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

A host-directed oxadiazole compound potentiates antituberculosis treatment via zinc poisoning in human macrophages and in a mouse model of infection

Antituberculosis drugs, mostly developed over 60 years ago, combined with a poorly effective vaccine, have failed to eradicate tuberculosis. More worryingly, multiresistant strains of Mycobacterium tuberculosis (MTB) are constantly emerging. Innovative strategies are thus urgently needed to improve tuberculosis treatment. Recently, host-directed therapy has emerged as a promising strategy to be used in adjunct with existing or future antibiotics, by improving innate immunity or limiting immunopathology. Here, using high-content imaging, we identified novel 1,2,4-oxadiazole-based compounds, which allow human macrophages to control MTB replication. Genome-wide gene expression analysis revealed that these molecules induced zinc remobilization inside cells, resulting in bacterial zinc intoxication. More importantly, we also demonstrated that, upon treatment with these novel compounds, MTB became even more sensitive to antituberculosis drugs, in vitro and in vivo, in a mouse model of tuberculosis. Manipulation of heavy metal homeostasis holds thus great promise to be exploited to develop host-directed therapeutic interventions.

Integrated virtual screening and in vitro studies for exploring the mechanism of triterpenoids in Chebulae Fructus alleviating mesaconitine-induced cardiotoxicity via TRPV1 channel

Background: In traditional Mongolian or Tibetan medicine in China, Chebulae Fructus (CF) is widely used to process or combine with aconitums to decrease the severe toxicity of aconitums. Researches in this area have predominantly focused on tannins, with few research on other major CF components for cardiotoxicity mitigation. The present study aimed to clarify whether triterpenoids can attenuate the cardiotoxicity caused by mesaconitine (MA) and investigate the mechanism of cardiotoxicity attenuation. Methods: Firstly, the pharmacophore model, molecular docking, and 3D-QSAR model were used to explore the mechanism of CF components in reducing the toxicity of MA mediated by the TRPV1 channel. Then three triterpenoids were selected to verify whether the triterpenoids had the effect of lowering the cardiotoxicity of MA using H9c2 cells combined with MTT, Hoechst 33258, and JC-1. Finally, Western blot, Fluo-3AM, and MTT assays combined with capsazepine were used to verify whether the triterpenoids reduced H9c2 cardiomyocyte toxicity induced by MA was related to the TRPV1 channel. Results: Seven triterpenoids in CF have the potential to activate the TRPV1 channel. And they exhibited greater affinity for TRPV1 compared to other compounds and MA. However, their activity was relatively lower than that of MA. Cell experiments revealed that MA significantly reduced H9c2 cell viability, resulting in diminished mitochondrial membrane potential and nuclear pyknosis and damage. In contrast, the triterpenoids could improve the survival rate significantly and counteract the damage of MA to the cells. We found that MA, arjungenin (AR), and maslinic acid (MSA) except corosolic acid (CRA) upregulated the expression of TRPV1 protein. MA induced a significant influx of calcium, whereas all three triterpenoids alleviated this trend. Blocking the TRPV1 channel with capsazepine only increased the cell viability that had been simultaneously treated with MA, and AR, or MSA. However, there was no significant difference in the CRA groups treated with or without capsazepine. Conclusion: The triterpenoids in CF can reduce the cardiotoxicity caused by MA. The MSA and AR function as TRPV1 agonists with comparatively reduced activity but a greater capacity to bind to TRPV1 receptors, thus antagonizing the excessive activation of TRPV1 by MA.

Spectroscopic, computational, cytotoxicity, and docking studies of 6-bromobenzimidazole as anti-breast cancer agent

6-Bromobenzimidazole (6BBZ) has been calculated in this study utilizing the 6-311++G(d,p) basis set and the Becke-3-Lee-Yang-Parr density functional approaches. The basic frequencies and geometric optimization are known. FTIR, FT-Raman, and UV-Vis spectra of the substance are compared between its computed and observed values. The energy gap between highest occupied molecular orbital-lowest unoccupied molecular orbital and molecule electrostatic potentials has been represented by charge density distributions that may be associated with the biological response. Time-dependent density functional theory calculations in the gas phase and dimethyl sulfoxide were carried out to ascertain the electronic properties and energy gap values using the same basis set. Molecular orbital contributions are investigated using the overlap population, partial, and total densities of states. Natural bond analysis was found to have strong electron delocalization by means of π(C4-C9) → π*(C5-C6), LP (N1) → π*(C7-C8), and LP(Br12) → π*(C5-C6) interactions. The Fukui function and Mulliken analysis have been explored on the atomic charges of the molecule. The nuclear magnetic resonance chemical shifts for 1 H and 13 C have been computed using the gauge-independent atomic orbital technique. With the highest binding affinity (-6.2 kcal mol-1 ) against estrogen sulfotransferase receptor (PDB ID: 1AQU) and low IC50 value of 17.23 μg/mL, 6BBZ demonstrated potent action against the MCF-7 breast cancer cell line. Studies on the antibacterial activity and ADMET prediction of the molecule have also been carried out.

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.

3D-SMGE: a pipeline for scaffold-based molecular generation and evaluation

In the process of drug discovery, one of the key problems is how to improve the biological activity and ADMET properties starting from a specific structure, which is also called structural optimization. Based on a starting scaffold, the use of deep generative model to generate molecules with desired drug-like properties will provide a powerful tool to accelerate the structural optimization process. However, the existing generative models remain challenging in extracting molecular features efficiently in 3D space to generate drug-like 3D molecules. Moreover, most of the existing ADMET prediction models made predictions of different properties through a single model, which can result in reduced prediction accuracy on some datasets. To effectively generate molecules from a specific scaffold and provide basis for the structural optimization, the 3D-SMGE (3-Dimensional Scaffold-based Molecular Generation and Evaluation) work consisting of molecular generation and prediction of ADMET properties is presented. For the molecular generation, we proposed 3D-SMG, a novel deep generative model for the end-to-end design of 3D molecules. In the 3D-SMG model, we designed the cross-aggregated continuous-filter convolution (ca-cfconv), which is used to achieve efficient and low-cost 3D spatial feature extraction while ensuring the invariance of atomic space rotation. 3D-SMG was proved to generate valid, unique and novel molecules with high drug-likeness. Besides, the proposed data-adaptive multi-model ADMET prediction method outperformed or maintained the best evaluation metrics on 24 out of 27 ADMET benchmark datasets. 3D-SMGE is anticipated to emerge as a powerful tool for hit-to-lead structural optimizations and accelerate the drug discovery process.

Combination of In Silico and In Vitro Screening to Identify Novel Glutamate Carboxypeptidase II Inhibitors

Glutamate carboxypeptidase II (GCPII) is a metalloprotease implicated in neurological diseases and prostate oncology. While several classes of potent GCPII-specific inhibitors exist, the development of novel active scaffolds with different pharmacological profiles remains a challenge. Virtual screening followed by in vitro testing is an effective means for the discovery of novel active compounds. Structure- and ligand-based pharmacophore models were created based on a dataset of known GCPII-selective ligands. These models were used in a virtual screening of the SPECS compound library (∼209.000 compounds). Fifty top-scoring virtual hits were further experimentally tested for their ability to inhibit GCPII enzymatic activity in vitro. Six hits were found to have moderate to high inhibitory potency with the best virtual hit, a modified xanthene, inhibiting GCPII with an IC50 value of 353 ± 24 nM. The identification of this novel inhibitory scaffold illustrates the applicability of pharmacophore-based modeling for the discovery of GCPII-specific inhibitors.

In Silico Testing of Some Protected Galactopyranose as SARS-CoV-2 Main Protease Inhibitors

An outbreak of novel Coronavirus disease (COVID-19 or 2019-nCoV) due to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has already demonstrated a fatal death toll all over the world. To cure this viral infection, a number of compounds of different categories have been investigated in silico. Some of the compounds showed better binding energy with COVID-19-related proteins. However, until now there is no appropriate drug except a vaccine. It was found that many antifungal drugs are used for COVID-19 patients in hospitals. Many monosaccharide esters have been reported to have antifungal potential. Thus, in the present study, some protected galactopyranose esters are chosen for molecular docking with SARS-CoV-2 main proteases (PDB id: 7BQY and 6LU7). A docking study revealed that galactopyranose esters 5-8 have very good docking scores (-8.4 to -6.5 kcal/mol) compared to the standard drugs azithromycin, remdesivir, and hydroxychloroquine. To explain such good scores interaction between amino acid residues of proteins and compounds in their docked complexes are calculated and duly discussed in this study.

Development of 3D-QSAR and pharmacophoric models to design new anti-Trypanosoma cruzi agents based on 2-aryloxynaphthoquinone scaffold

In this work we have collected a set of 30 trypanosomicidal naphthoquinones and developed pharmacophoric and 3D-QSAR models as tools for the design of new potential anti-Chagasic compounds. Firstly, qualitative information was obtained from SAR and pharmacophoric models identifying some fragments around the 2-aryloxynaphthoquinone scaffold important for the antiparasitic activity. Then, 3D-QSAR CoMFA and CoMSIA models were developed. The models showed adequate statistical parameters where the steric, electrostatic, and hydrophobic features explain the trypanosomicidal effect. Therefore, to validate our models, we carried out the design, synthesis, and biological evaluation on T. cruzi epimastigotes of five new compounds (33a-e). According to CoMFA model, three out of five compounds showed pIC₅₀ values within one logarithmic unit of deviation. The two compounds that did not fit the predictions were those with high lipophilicity, which agreed with the SAR and pharmacophore models. Docking and molecular dynamic studies were performed on T. cruzi trypanothione reductase, in a proposed binding site for this type of naphthoquinone. Interestingly, 33a-e showed the same interaction pattern as a naphthoquinone inhibitor (2). Finally, predicted drug-likeness properties indicated that 33a-e have optimal oral bioavailability. Thus, this study provides new in silico models for obtaining novel trypanosomicidal compounds.

Zebrafish Is a Powerful Tool for Precision Medicine Approaches to Neurological Disorders

Personalized medicine is currently one of the most promising tools which give hope to patients with no suitable or no available treatment. Patient-specific approaches are particularly needed for common diseases with a broad phenotypic spectrum as well as for rare and yet-undiagnosed disorders. In both cases, there is a need to understand the underlying mechanisms and how to counteract them. Even though, during recent years, we have been observing the blossom of novel therapeutic techniques, there is still a gap to fill between bench and bedside in a patient-specific fashion. In particular, the complexity of genotype-to-phenotype correlations in the context of neurological disorders has dampened the development of successful disease-modifying therapeutics. Animal modeling of human diseases is instrumental in the development of therapies. Currently, zebrafish has emerged as a powerful and convenient model organism for modeling and investigating various neurological disorders. This model has been broadly described as a valuable tool for understanding developmental processes and disease mechanisms, behavioral studies, toxicity, and drug screening. The translatability of findings obtained from zebrafish studies and the broad prospect of human disease modeling paves the way for developing tailored therapeutic strategies. In this review, we will discuss the predictive power of zebrafish in the discovery of novel, precise therapeutic approaches in neurosciences. We will shed light on the advantages and abilities of this in vivo model to develop tailored medicinal strategies. We will also investigate the newest accomplishments and current challenges in the field and future perspectives.

Engineering ACE2 decoy receptors to combat viral escapability

Decoy receptor proteins that trick viruses to bind to them should be resistant to viral escape because viruses that require entry receptors cannot help but bind decoy receptors. Angiotensin-converting enzyme 2 (ACE2) is the major receptor for coronavirus cell entry. Recombinant soluble ACE2 was previously developed as a biologic against acute respiratory distress syndrome (ARDS) and verified to be safe in clinical studies. The emergence of COVID-19 reignited interest in soluble ACE2 as a potential broad-spectrum decoy receptor against coronaviruses. In this review, we summarize recent developments in preclinical studies using various high-affinity mutagenesis and Fc fusion approaches to achieve therapeutic efficacy of recombinant ACE2 decoy receptor against coronaviruses. We also highlight the relevance of stimulating effector immune cells through Fc-receptor engagement and the potential of using liquid aerosol delivery of ACE2 decoy receptors for defense against ACE2-utilizing coronaviruses.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Regioselectivity mechanism of the Thunbergia alata Δ6-16:0-acyl carrier protein desaturase

Plant plastidial acyl-acyl carrier protein (ACP) desaturases are a soluble class of diiron-containing enzymes that are distinct from the diiron-containing integral membrane desaturases found in plants and other organisms. The archetype of this class is the stearoyl-ACP desaturase which converts stearoyl-ACP into oleoyl (18:1Δ9cis)-ACP. Several variants expressing distinct regioselectivity have been described including a Δ6-16:0-ACP desaturase from black-eyed Susan vine (Thunbergia alata). We solved a crystal structure of the T. alata desaturase at 2.05 Å resolution. Using molecular dynamics (MD) simulations, we identified a low-energy complex between 16:0-ACP and the desaturase that would position C6 and C7 of the acyl chain adjacent to the diiron active site. The model complex was used to identify mutant variants that could convert the T. alata Δ6 desaturase to Δ9 regioselectivity. Additional modeling between ACP and the mutant variants confirmed the predicted regioselectivity. To validate the in-silico predictions, we synthesized two variants of the T. alata desaturase and analyzed their reaction products using gas chromatography-coupled mass spectrometry. Assay results confirmed that mutants designed to convert T. alata Δ6 to Δ9 selectivity exhibited the predicted changes. In complementary experiments, variants of the castor desaturase designed to convert Δ9 to Δ6 selectivity lost some of their Δ9 desaturation ability and gained the ability to desaturate at the Δ6 position. The computational workflow for revealing the mechanistic understanding of regioselectivity presented herein lays a foundation for designing acyl-ACP desaturases with novel selectivities to increase the diversity of monoenes available for bioproduct applications.

Prediction of ITK inhibitor kinases activity based on posterior probabilistic weighted average based ensemble voting classification

ITK inhibitor is used for the treatment of asthma and activity of inhibitor prediction helps to provide better treatment. Few researches were carried out for the analysis and prediction of kinases activity. Existing methods applied for the inhibitor prediction have limitations of imbalance dataset and lower performance. In this research, the Posterior Probabilistic Weighted Average Based Ensemble voting (PPWAE)ensemble method is proposed with various classifier for effective prediction of kinases activity. The PPWAE model selects the most probable class from the classification method for prediction. The co-train model has two advantages: Features are trained together to increases the learning rate of model and probability is measured for each model to select the efficient classifier. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Classification and Regression Tree (CART), and Nave Bayes were among the classifiers employed. The results suggest that the Probabilistic Co-train ensemble technique performs well in kinase activity prediction. In the prediction of ITK inhibitor activity, the suggested ensemble method has a 74.27 percent accuracy, while the conventional SVM method has a 60% accuracy.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

A Systematic Review on the Contribution of Artificial Intelligence in the Development of Medicines for COVID-2019

Background: COVID-2019 pandemic lead to a raised interest on the development of new treatments through Artificial Intelligence (AI). Aim: to carry out a systematic review on the development of repurposed drugs against COVID-2019 through the application of AI. Methods: The Systematic Reviews and Meta-Analyses (PRISMA) checklist was applied. Keywords: [“Artificial intelligence” and (COVID or SARS) and (medicine or drug)]. Databases: PubMed^®, DOAJ and SciELO. Cochrane Library was additionally screened to identify previous published reviews on the same topic. Results: From the 277 identified records [PubMed^® (n = 157); DOAJ (n = 119) and SciELO (n = 1)], 27 studies were included. Among other, the selected studies on new treatments against COVID-2019 were classified, as follows: studies with in-vitro and/or clinical data; association of known drugs; and other studies related to repurposing of drugs. Conclusion: Diverse potentially repurposed drugs against COVID-2019 were identified. The repurposed drugs were mainly from antivirals, antibiotics, anticancer, anti-inflammatory, and Angiotensin-converting enzyme 2 (ACE2) groups, although diverse other pharmacologic groups were covered. AI was a suitable tool to quickly analyze large amounts of data or to estimate drug repurposing against COVID-2019.