Tumor Microenvironment: From Cervical Carcinogenesis to Therapeutic Advancements.

Cervical cancer has become a major worldwide health concern that demands attention to women's health and often needs more effective and specialized treatment options. Cervical cancer claims the lives of over 300,000 women globally, ranking as the fourth most prevalent cancer among women. The tumor microenvironment (TME) shapes a distinctive landscape for tumor survival, characterized by factors like immunosuppression, hypoxia, acidity, and nutrient scarcity. Comprising tumor cells, immune cells, mesenchymal cells, cancer-associated fibroblasts, and extracellular matrix, the TME reprograms key aspects of tumor development, uncontrolled proliferation, invasion, metastasis, and response to treatments. Recognizing the TME's pivotal role in tumor progression and treatment responsiveness, targeting the TME has emerged as a potential strategy in cancer therapy. This publication delves into recent TME research, offering a comprehensive overview of the specific functions of each TME component in cancer development and progression. Based on the reviewed literature, it appears that women with cervical cancer may benefit from more effective therapy, fewer side effects, and a higher quality of life in the future. By addressing pressing problems and unmet needs in the field, this review has the potential to significantly alter the course of cervical cancer treatment in the future. Furthermore, it outlines the primary therapeutic targets identified by researchers, which may prove valuable in treating tumors.

Targeting prostate cancer via therapeutic targeting of PIM-1 kinase by Naringenin and Quercetin.

PIM-1 kinase belongs to the Ser/Thr kinases family, an attractive therapeutic target for prostate cancer. Here, we screened about 100 natural substances to find potential PIM-1 inhibitors. Two natural compounds, Naringenin and Quercetin, were finally selected based on their PIM-1 inhibitory potential and binding affinities. The docking score of Naringenin and Quercetin with PIM-1 is -8.4 and - 8.1 kcal/mol, respectively. Fluorescence binding studies revealed a strong affinity (Ka values, 3.1 × 104 M-1 and 4.6 × 107 M-1 for Naringenin and Quercetin, respectively) with excellent IC50 values for Naringenin and Quercetin (28.6 µM and 34.9 µM, respectively). Both compounds inhibited the growth of prostate cancer cells (LNCaP) in a dose-dependent manner, with the IC50 value of Naringenin at 17.5 µM and Quercetin at 8.88 µM. To obtain deeper insights into the PIM-1 inhibitory effect of Naringenin and Quercetin, we performed extensive molecular dynamics simulation studies, which provided insights into the binding mechanisms of PIM-1 inhibitors. Finally, Naringenin and Quercetin were suggested to serve as potent PIM-1 inhibitors, offering targeted treatments of prostate cancer. In addition, our findings may help to design novel Naringenin and Quercetin derivatives that could be effective in therapeutic targeting of prostate cancer.

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA.

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Drug repurposing for cancer.

In the dynamic landscape of cancer therapeutics, the innovative strategy of drug repurposing emerges as a transformative paradigm, heralding a new era in the fight against malignancies. This book chapter aims to embark on the comprehension of the strategic deployment of approved drugs for repurposing and the meticulous journey of drug repurposing from earlier times to the current era. Moreover, the chapter underscores the multifaceted and complex nature of cancer biology, and the evolving field of cancer drug therapeutics while emphasizing the mandate of drug repurposing to advance cancer therapeutics. Importantly, the narrative explores the latest tools, technologies, and cutting-edge methodologies including high-throughput screening, omics technologies, and artificial intelligence-driven approaches, for shaping and accelerating the pace of drug repurposing to uncover novel cancer therapeutic avenues. The chapter critically assesses the breakthroughs, expanding the repertoire of repurposing drug candidates in cancer, and their major categories. Another focal point of this book chapter is that it addresses the emergence of combination therapies involving repurposed drugs, reflecting a shift towards personalized and synergistic treatment approaches. The expert analysis delves into the intricacies of combinatorial regimens, elucidating their potential to target heterogeneous cancer populations and overcome resistance mechanisms, thereby enhancing treatment efficacy. Therefore, this chapter provides in-depth insights into the potential of repurposing towards bringing the much-needed big leap in the field of cancer therapeutics.

Harnessing machine learning potential for personalised drug design and overcoming drug resistance.

Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process - Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.

Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis.

Two of the deadliest infectious diseases, COVID-19 and tuberculosis (TB), have combined to establish a worldwide pandemic, wreaking havoc on economies and claiming countless lives. The optimised, multitargeted medications may diminish resistance and counter them together. Based on computational expression studies, 183 genes were co-expressed in COVID-19 and TB blood samples. We used the multisampling screening algorithms on the top ten co-expressed genes (CD40, SHP2, Lysozyme, GATA3, cCBL, SIVmac239 Nef, CD69, S-adenosylhomocysteinase, Chemokine Receptor-7, and Membrane Protein). Imidurea is a multitargeted inhibitor for COVID-19 and TB, as confirmed by extensive screening and post-filtering utilising MM\GBSA algorithms. Imidurea has shown docking and MM\GBSA scores of -8.21 to -4.75 Kcal/mol and -64.16 to -29.38 Kcal/mol, respectively. The DFT, pharmacokinetics, and interaction patterns suggest that Imidurea may be a drug candidate, and all ten complexes were tested for stability and bond strength using 100 ns for all MD atoms. The modelling findings showed the complex's repurposing potential, with a cumulative deviation and fluctuation of <2 Å and significant intermolecular interaction, which validated the possibilities. Finally, an inhibition test was performed to confirm our in-silico findings on SARS-CoV-2 Delta variant infection, which was suppressed by adding imidurea to Vero E6 cells after infection.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives.

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing.

The ever-changing environmental conditions and pollution are the prime reasons for the onset of several emerging and re-merging diseases. This demands the faster designing of new drugs to curb the deadly diseases in less waiting time to cure the animals and humans. Drug molecules interact with only protein surface on specific locations termed as ligand binding sites (LBS). Therefore, the knowledge of LBS is required for rational drug designing. Existing geometrical LBS prediction methods rely on search of cavities based on the fact that 83% of the LBS found in deep cavities, however, these methods usually fail where LBS localize outside deep cavities. To overcome this challenge, the present work provides an artificial neural network (ANN) based method to predict LBS outside deep cavities in animal proteins including human to facilitate drug designing. In the present work a feed-forward backpropagation neural network was trained by utilizing 38 structural, atomic, physiochemical, and evolutionary discriminant features of LBS and non-LBS residues localized in the extracted roughest patch on protein surface. The performance of this ANN based prediction method was found 76% better for those proteins where cavity subspace (extracted by MetaPocket 2.0, a consensus method) failed to predict LBS due to their localization outside the deep cavities. The prediction of LBS outside deep cavities will facilitate in drug designing for the proteins where it is not possible due to lack of LBS information as the geometrical LBS prediction methods rely on extraction of deep cavities.

Translational bioinformatics approach to combat cardiovascular disease and cancers.

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Computational assessment of marine natural products as LasR inhibitors for attenuating quorum sensing in Pseudomonas aeruginosa.

The Quorum Sensing (QS) system in bacteria has become a focal point for researchers aiming to develop novel antimicrobials to combat multidrug-resistant bacteria. Pseudomonas aeruginosa, an opportunistic Gram-negative bacterium, has developed resistance against a variety of antimicrobial agents, making it a formidable pathogen responsible for nosocomial infections. QS system mainly controls the expression of genes responsible for biofilm formation and virulence of bacteria. Within the QS system of P. aeruginosa, the transcription activator LasR plays a pivotal role and is an appealing target for the development of antimicrobial agents. In this study, we employed molecular docking and molecular dynamics simulations to identify potential inhibitors of LasR by screening marine natural products (MNPs) from the CMNPD database. We identified ten MNPs with excellent docking scores (less than -11.7 kcal/mol) against LasR, surpassing the binding energy of the co-crystal 3-oxo-C12-HSL (-8.594 kcal/mol) and the reference compound cladodionen (-6.71 kcal/mol). Furthermore, we selected five of these MNPs with the highest MM/GBSA binding energies for extensive 100 ns molecular simulations to assess their stability. The molecular dynamics simulations indicated three MNPs, namely CMNPD10886, CMNPD20987, and CMNPD20960, maintained high stability throughout the 100 ns simulation period, as evidenced by their root mean square deviation, root mean square fluctuation, radius of gyration, and hydrogen bond interactions within the ligand-protein complex analysis. Furthermore, essential dynamics (PCA and DCCM) were performed to analyse the correlated motion of amino acids. These findings suggest that these compounds hold potential as inhibitors of LasR, offering promising prospects for the development of treatments against infections.Communicated by Ramaswamy H. Sarma.

Delineated 3-1-BenCarMethInYlPro-Phosphonic Acid's Adroit Activity against Lung Cancer through Multitargeted Docking, MM\GBSA, QM-DFT and Multiscale Simulations.

Lung cancer is a pervasive and challenging disease with limited treatment options, with global health challenges often present with complex molecular profiles necessitating the exploration of innovative therapeutic strategies. Single-target drugs have shown limited success due to the heterogeneity of this disease. Multitargeted drug designing is imperative to combat this complexity by simultaneously targeting multiple target proteins and pathways, which can enhance treatment efficacy and overcome resistance by addressing the dynamic nature of the disease and stopping tumour growth and spread. In this study, we performed the molecular docking studies of Drug Bank compounds with a multitargeted approach against crucial proteins of lung cancer such as heat shock protein 5 (BIP/GRP78) ATPase, myosin 9B RhoGAP, EYA2 phosphatase inhibitor, RSK4 N-terminal kinase, and collapsin response mediator protein-1 (CRMP-1) using HTVS, SP with XP algorithms, and poses were filtered using MM\GBSA which identified [3-(1-Benzyl-3-Carbamoylmethyl-2-Methyl-1h-Indol-5-Yloxy)-Propyl-]-Phosphonic Acid (3-1-BenCarMethIn YlPro-Phosphonic Acid) (DB02504) as multitargeted drug candidate with docking and MM\GBSA score ranges from -5.83 to -10.66 and -7.56 to -50.14 Kcal/mol, respectively. Further, the pharmacokinetic and QM-based DFT studies have shown complete acceptance results, and interaction fingerprinting reveals that ILE, GLY, VAL, TYR, LEU, and GLN were among the most interacting residues. The 100 ns MD simulation in the SPC water model with NPT ensemble showed stable performance with deviation and fluctuations <2 Å with huge interactions, making it a promising multitargeted drug candidate; however, experimental studies are needed before use.

Drug Repositioning Using Computer-aided Drug Design (CADD).

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Discovery of Tropomyosin Receptor Kinase Inhibitors as New Generation Anticancer Agents: A Review.

BACKGROUND: The tropomyosin receptor kinases (TRKs) are crucial for many cellular functions, such as growth, motility, differentiation, and metabolism. Abnormal TRK signalling contributes to a variety of human disorders, most evidently cancer. Comprehensive genomic studies have found numerous changes in the genes that code for TRKs like MET, HER2/ErbB2, and EGFR, among many others. Precision medicine resistance, relapse occurring because of the protein point mutations, and the existence of multiple molecular feedback loops are significant therapeutic hurdles to the long-term effectiveness of TRK inhibitors as general therapeutic agents for the treatment of cancer. OBJECTIVE: This review is carried out to highlight the role of tropomyosin receptor kinase in cancer and the function of TRK inhibitors in the intervention of cancer. METHODS: Literature research has been accomplished using Google Scholar and databases like ScienceDirect, WOS, PubMed, SciFinder, and Scopus. RESULTS: In this review, we provide an overview of the main molecular and functional properties of TRKs and their inhibitors. It also discusses how these advancements have affected the development and use of novel treatments for malignancies and other conditions caused by activated TRKs. Several therapeutic strategies, including the discovery and development of small-molecule TRK inhibitors belonging to various chemical classes and their activity, as well as selectivity towards the receptors, have been discussed in detail. CONCLUSION: This review will help the researchers gain a fundamental understanding of TRKs, how this protein family works, and the ways to create chemical moieties, such as TRK inhibitors, which can serve as tailored therapies for cancer.

2D-QSAR, molecular docking and MD simulation based virtual screening of the herbal molecules against Alzheimer's disorder: an approach to predict CNS activity.

Acetylcholinesterase (AChE) is one of the key enzyme targets that have been used clinically for the management of Alzheimer's Disorder (AD). Numerous reports in the literature predict and demonstrate in-vitro, and in-silico anticholinergic activity of herbal molecules, however, majority of them failed to find clinical application. To address these issues, we developed a 2D-QSAR model that could efficiently predict the AChE inhibitory activity of herbal molecules along with predicting their potential to cross the blood-brain-barrier (BBB) to exert their beneficial effects during AD. Virtual screening of the herbal molecules was performed and amentoflavone, asiaticoside, astaxanthin, bahouside, biapigenin, glycyrrhizin, hyperforin, hypericin, and tocopherol were predicted as the most promising herbal molecules for inhibiting AChE. Results were validated through molecular docking, atomistic molecular dynamics simulations and Molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) studies against human AChE (PDB ID: 4EY7). To determine whether or not these molecules can cross BBB to inhibit AChE within the central nervous system (CNS) for being beneficial for the management of AD, we determined a CNS Multi-parameter Optimization (MPO) score, which was found in the range of 1 to 3.76. Overall, the best results were observed for amentoflavone and our results demonstrated a PIC50 value of 7.377 nM, molecular docking score of -11.5 kcal/mol, and CNS MPO score of 3.76. In conclusion, we successfully developed a reliable and efficient 2D-QSAR model and predicted amentoflavone to be the most promising molecule that could inhibit human AChE enzyme within the CNS and could prove beneficial for the management of AD.Communicated by Ramaswamy H. Sarma.

De novo drug designing coupled with brute force screening and structure guided lead optimization gives highly specific inhibitor of METTL3: a potential cure for Acute Myeloid Leukaemia.

Expression of METTL3, a SAM dependent methyltransferase, which deposits m6A on mRNA is linked to poor prognosis in Acute Myeloid Leukaemia and other type of cancers. Down regulation of this epitranscriptomic regulator has been found to inhibit cancer progression. Silencing the methyltransferase activity of METTL3 is a lucrative strategy to design anticancer drugs. In this study 3600 commercially available molecules were screened against METTL3 using brute force screening approach. However, none of these compounds take advantage of the unique Y-shaped binding cavity of the protein, raising the need for de novo drug designing strategies. As such, 125 branched, Y-shaped molecules were designed by "stitching" together the chemical fragments of the best inhibitors that interact strongly with the METTL3 binding pocket. This results in molecules that have the three-dimensional structure and functional groups which enable it to fit in the METTL3 cavity like fingers in a glove, having unprecedented selectivity and binding affinities. The designed compounds were further refined based on Lipinski's rule, docking score and synthetic accessibility. The molecules faring well in these criteria were simulated for 100 ns to check the stability of the protein inhibitor complex followed by binding free energy calculation.Communicated by Ramaswamy H. Sarma.

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

Origin of Antibiotics and Antibiotic Resistance, and Their Impacts on Drug Development: A Narrative Review.

Antibiotics have revolutionized medicine, saving countless lives since their discovery in the early 20th century. However, the origin of antibiotics is now overshadowed by the alarming rise in antibiotic resistance. This global crisis stems from the relentless adaptability of microorganisms, driven by misuse and overuse of antibiotics. This article explores the origin of antibiotics and the subsequent emergence of antibiotic resistance. It delves into the mechanisms employed by bacteria to develop resistance, highlighting the dire consequences of drug resistance, including compromised patient care, increased mortality rates, and escalating healthcare costs. The article elucidates the latest strategies against drug-resistant microorganisms, encompassing innovative approaches such as phage therapy, CRISPR-Cas9 technology, and the exploration of natural compounds. Moreover, it examines the profound impact of antibiotic resistance on drug development, rendering the pursuit of new antibiotics economically challenging. The limitations and challenges in developing novel antibiotics are discussed, along with hurdles in the regulatory process that hinder progress in this critical field. Proposals for modifying the regulatory process to facilitate antibiotic development are presented. The withdrawal of major pharmaceutical firms from antibiotic research is examined, along with potential strategies to re-engage their interest. The article also outlines initiatives to overcome economic challenges and incentivize antibiotic development, emphasizing international collaborations and partnerships. Finally, the article sheds light on government-led initiatives against antibiotic resistance, with a specific focus on the Middle East. It discusses the proactive measures taken by governments in the region, such as Saudi Arabia and the United Arab Emirates, to combat this global threat. In the face of antibiotic resistance, a multifaceted approach is imperative. This article provides valuable insights into the complex landscape of antibiotic development, regulatory challenges, and collaborative efforts required to ensure a future where antibiotics remain effective tools in safeguarding public health.

Prodrug Rewards in Medicinal Chemistry: An Advance and Challenges Approach for Drug Designing.

This article emphasizes the importance of prodrugs and their diverse spectrum of effects in the field of developing novel drugs for a variety of biological applications. Prodrugs are chemicals that are supplied inactively, but then go through enzymatic and chemical transformation in vivo to release the active parent medication that can have the desired pharmacological effect. By adding an inactive chemical moiety, prodrugs are improved in a number of ways that contribute to their potency and durability. For the purpose of illustrating the usefulness of the prodrug approach, this review covers examples of prodrugs that have been made available or are now undergoing human trials. Additionally, it included lists of the most common functional groups, carrier linkers, and reactive chemicals that can be used to create prodrugs. The current study also provides a brief introduction, several chemical methods and modifications for creating prodrugs and mutual prodrugs, as well as an explanation of recent advancements and difficulties in the field of prodrug design. The primary chemical carriers employed in the creation of prodrugs, such as esters, amides, imides, NH-acidic carriers, amines, alcohols, carbonyl, carboxylic, and azo-linkages, are also discussed. This review also discusses glycosidic and triglyceride mutually activated prodrugs, which aim to deliver the drugs after bioconversion at the intended site of action. The article also discusses the extensive chemistry and wide variety of applications of recently approved prodrugs, such as antibacterial, anti-inflammatory, cardiovascular, antiplatelet, antihypertensive, atherosclerotic, antiviral, etc. In order to illustrate the prodrug and mutual drug concept's various applications and highlight its many triumphs in overcoming the formulation and delivery of problematic pharmaceuticals, this work represents a thorough guide that includes the synthetic moiety for the reader.

Applications of QSAR study in drug design of tubulin binding inhibitors.

New drug discovery is recognized as a complicated, costly, time-consuming, and difficult process. Computer-aided drug discovery has developed as a potent and promising method for faster, cheaper, and more effective drug creation. Recently, the rapid rise of computational methods for drug discovery, including anticancer medicines, had a substantial and exceptional impact on anticancer drug design, as well as providing beneficial insights into the field of cancer therapy. In this paper, we discussed the quantitative structure-activity relationship (QSAR), which is a significant in-silico tool in rational drug design. The QSAR method is used to optimize the existing leads to improve their biological activities, and physicochemical properties and to predict the biological activities of untested and sometimes unavailable compounds, so QSAR is a significant method in drug designing. This article is a comprehensive review of various QSAR studies conducted which help to create new and potent inhibitors for targeting tubulin, a crucial target in cancer treatment. It particularly focuses on studies that provide structural insights into the compounds targeting tubulin. It should prioritize continually researching specific scaffolds, with a focus on important attachment regions, to gather more powerful molecular data and enhance models. This will lead to a better understanding of drug interactions and the development of improved cancer-targeting inhibitors for tubulin.Communicated by Ramaswamy H. Sarma.