Drug Discovery Research Articles

AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics in Biopharmaceuticals

Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential.

Organoids: development and applications in disease models, drug discovery, precision medicine, and regenerative medicine

Organoids: development and applications in disease models, drug discovery, precision medicine, and regenerative medicine

Artificial intelligence as a tool in drug discovery and development

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Bioinformatics approaches to explore the discovery of new plant-derived drugs: a brief review

Bioinformatics approaches to explore the discovery of new plant-derived drugs: a brief review

LC-MS-Based Targeted Metabolite Profiling and Atomic Absorption Spectrophotmeter Based Mineral Analysis of Methanol Extract of Terminalia bellerica (Gaertn.) Leaves

Aim: Natural products in medicinal plants are one of the important sources for modern drug discovery. Plant-based formulations play an important role in management of numerous diseases. To discover the medical potential of natural products, it is important to understand the bioactive compounds present in medicinal plants. Hence the aim of the study is to identify the volatile compounds by LC –MS analysis and minerals by AAS assay present in the leaves of Terminalia bellerica. Study Design: This study investigates metabolites present in leaves of Terminalia bellerica have been studied by AAS and LC-MS techniques. Results: The AAS assay showed that the leaves of T. bellerica are particularly rich in iron (260 mg/kg) compared to their zinc, manganese, and copper content. Meanwhile, the LC-MS analysis identified chebulinic acid, chebulagic acid, punicafolin, myricetin, rutin, and galloyl punicalagin as being present in the highest concentrations. Conclusion: Results highlighted presence of considerable amount of iron and other minerals in methanolic extract of the leaves of T. bellerica, it may be used in the development of health supplements. The results also revealed that bioactive compounds in the methanolic extracts of T. bellerica leave used for drug design and making it safe to human health.

Network pharmacology, molecular docking, and in vitro study on Aspilia pluriseta against prostate cancer

BackgroundCurrent prostate cancer treatments are associated with life-threatening side effects, prompting the search for effective and safer alternatives. Aspilia pluriseta Schweinf. ex Engl. has previously shown anticancer activity in lung and liver cancer cell lines. This study investigated its potential for prostate cancer.MethodsA crude extract of A. pluriseta root was prepared using dichloromethane/methanol (1:1 v/v) and partitioned into hexane, ethyl acetate, and water fractions. The MTT assay was used to assess the antiproliferative activity of the fractions. The active fractions were tested at 6.25–200 µg/ml on human prostate cancer DU-145 cells and non-cancerous Vero E6 cells. Qualitative phytochemical and gas chromatography-mass spectrometry (GC-MS) analyses were conducted to identify chemical compounds. Network pharmacology was employed to predict molecular targets and modes of action of the identified chemical compounds, with subsequent validation through molecular docking and real-time PCR.ResultsActive extracts included crude dichloromethane/methanol, hexane, and ethyl acetate fractions, inhibiting DU-145 cell proliferation with IC50 values of 16.94, 20.06, and 24.14 µg/ml, respectively. Selectivity indices were determined to be 6.04 (crude), 3.62 (hexane), and 6.68 (ethyl acetate). Identified phytochemicals comprised phenols, terpenoids, flavonoids, tannins, sterols, and saponins. GC-MS analysis revealed seventy-nine (79) compounds, with seven (7) meeting ideal drug candidate parameters; their hub gene targets included MAPK3, MAPK1, IL6, TP53, ESR1, PTGS2, MMP9, MDM2, AR, and MAP2K1, implicating regulation of PI3K/Akt, MAPK, and p53 signaling pathways as potential modes of action. Core compounds such as 1-heneicosanol, lanosterol, andrographolide, and retinoic acid exhibited strong binding activities, particularly lanosterol with MAPK21 (-9.7 kcal/mol), ESR1 (-8.9 kcal/mol), and MAPK3 (-8.8 kcal/mol). Treatment with A. pluriseta downregulated AR expression and upregulated p53, while also downregulating CDK1 and BCL-2 and upregulating caspase-3.ConclusionsA. pluriseta extracts inhibited DU-145 cell growth without causing cellular toxicity, suggesting great potential for development as an anti-prostate cancer agent. However, further in vitro and in vivo experiments are recommended.

A Computational Perspective into Binding Mechanism of a potent FAK Inhibitor as Anticancer Drug Candidate: Insights from Molecular Dynamics Simulations

A Computational Perspective into Binding Mechanism of a potent FAK Inhibitor as Anticancer Drug Candidate: Insights from Molecular Dynamics Simulations

Cancer pharmacoinformatics: Databases and analytical tools.

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Calculated hydration free energies become less accurate with increases in molecular weight

In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect–the hydration free energy (HFE) of small molecules–is often insufficiently analyzed. While most benchmarking studies report excellent accuracies of calculated hydration free energies–usually within 2 kcal/mol of experimental values–we find that deeper analysis reveals significant shortcomings. Herein, we report a dependence of HFE prediction errors on ligand molecular weight–the higher the weight, the bigger the prediction error and the higher the probability the calculated result is erroneous by a large amount. We show that in the drug-like molecular weight region, HFE predictions can easily be off by 5 kcal/mol or more. This is likely to be highly problematic in a drug discovery and development setting. We make our HFE results and molecular descriptors freely and fully available in order to encourage deeper analysis of future molecular dynamics results and facilitate development of the next generation of force fields.

A comprehensive review on the development of chiral Cu, Ni, and Zn complexes as pharmaceutical agents over the past decades: Synthesis, molecular structure and biological activity.

Chirality is a fundamental and widespread geometric structural property in living organisms that most biomacromolecules including nucleic acids, proteins and enzymes, possess. Consequently, the development of chiral drugs capable of binding specific targets have gradually gained wide attention in recent decades due to their selective effects on a broad spectrum of biological events ranging from cell metabolism to cell fate. In this context, the synthesis of chiral compounds as promising therapeutic candidates has assumed a major role in drug discovery. Among them, chiral metal complexes have attracted considerable interest due to their unique and intriguing structural features that could enable overcoming side effects and drug-resistance phenomena of metal-based drugs currently in the market such as cisplatin. In the current scenario, an in-depth overview of non-platinum chiral complexes needs to be presented and carried forward. Therefore, in this perspective article, an update of the scientific development of bioactive chiral copper, zinc and nickel complexes have been reported since they have not been thoroughly reviewed so far. Specifically, we focused the article mainly on metal complexes containing chiral ligands (type 2 chirality) as in literature they are more numerous than those with chirality at the metal center (type 1 chirality). Herein, not only their biological activity but also their mechanism of action is summarized. Furthermore, in the final section of the article we have highlighted copper-based complexes as those with a superior biological activity profile and greater prospects for development as a drug.

Repurposing Antiviral Drugs as Potential Anti-EGFR Agents in NSCLC: A Structure-Based Screening and Molecular Dynamics Analysis.

One of the problems resulting from recurrent hyperactivated or mutant epidermal growth factor receptors (EGFR) in non-small cell lung cancer (NSCLC) is therapeutic resistance. Consequently, this leads to increased expressionof oncogenicproteins and reduces the efficacy of EGFR tyrosine kinase inhibitors (TKIs). This study assessed antiviral drug efficacy as potential anti-EGFR agentsfor NSCLC. We used structure-based virtual screening to evaluate 66 antiviral drugs thoroughly. The top 6 antiviral drugs exhibiting impressive binding energies (i.e. surpassing a threshold of -8.5 kcalmol-1) were identified. Subsequent bioactivity analysis and ADMET profiling were performed to select the most promising candidates, followed by a molecular dynamic simulation. Among the selected antiviral regimens, dolutegravir demonstrated the highest docking score (-9.8 kcalmol-1), followed by rilpivirine and ensitrelvir, surpassing other candidates and our reference EGFR TKI. Further molecular dynamics simulations revealed promising dynamic interactions of dolutegravir, ensitrelvir, and rilpivirine with the EGFR target as compared with afatinib. Our findings highlight the repositioning potential of antiviral drugs for anti-EGFR drug discovery, supported by their robust docking scores, ADMET profiles, dynamic interactions, and binding free energies. The results open up new avenues for advanced NSCLC therapy. Further in vitro investigations are warranted to evaluate their efficacy and safety.

Deciphering Chemical Rules for Drug Penetration into Strongyloides

Background: Strongyloidiasis, a parasitic infection, presents a significant public health challenge in tropical regions due to the limited repertoire of effective treatments. The screening of chemical libraries against the therapeutically relevant third-stage larvae (L3) of the model parasite Strongyloides venezuelensis yielded meager success rates. This situation is reminiscent of Gram-negative bacteria, where drug entry is a limiting factor. Methods: Here, we set out to determine whether similar barriers are in place and establish whether structural and property requirements exist for anti-strongyloides drug discovery. We focused on dyes as their uptake and effects on viability can be independently assessed in the multicellular parasite, thus providing a means to study the possibility of similar entry rules. We tested different dyes in in vitro assays on L3s. Results: We found that staining was necessary to reduce parasite viability, with some dyes achieving anti-strongyloides effects at concentrations similar to those of the reference drug, ivermectin (IV). Some dyes also showed activity against female adults at concentrations well below that of ivermectin. Unfortunately, the most potent dye, Methylene Blue, was unable to prevent the infection in a preliminary in vivo mouse model assay, presumably due to fast dye clearance. Structural analysis showed that positive charges facilitated the access of the compounds to the L3 tissue, thus providing a structural tool for the introduction of activity. For female adults, low globularity is additionally required. As a proof of concept, we added a positive charge to an inactive compound of one of our chemical libraries and re-determined the activity. Conclusions: These findings allow us to establish structural rules for parasite entry that could be of interest for future drug screening or drug development campaigns. These rules might also be applicable to other related parasites.

Atypical sphingosine-1-phosphate metabolites—biological implications of alkyl chain length

AbstractSphingosine-1-phosphate (S1P) is a bioactive lipid signaling molecule with pleiotropic implications by both auto- and paracrine signaling. Signaling occurs by engaging five G protein-coupled receptors (S1P1-5) or intracellular pathways. While the extensively studied S1P with a chain length of 18 carbon atoms (d18:1 S1P) affects lymphocyte trafficking, immune cell survival and inflammatory responses, the biological implication of atypical S1Ps such as d16:1 or d20:1 remains elusive. As S1P lipids have far-reaching implications in health and disease states in mammalian organisms, the previous contrasting results may be attributed to differences in S1P’s alkyl chain length. Current research is beginning to appreciate these less abundant atypical S1P moieties. This review provides an up-to-date foundation of recent findings on the biological implications of atypical S1P chain lengths and offers a perspective on future research endeavors on S1P alkyl chain length–influenced signaling and its implications for drug discovery.

Structure‐Agnostic Bioactivity‐Driven Combinatorial Biosynthesis Reveals New Antidiabetic and Anticancer Triterpenoids

Although combinatorial biosynthesis can dramatically expand the chemical structures of bioactive natural products to identify molecules with improved characteristics, progress in this direction has been hampered by the difficulty in isolating and characterizing the numerous produced compounds. This challenge could be overcome with improved designs that enable the analysis of the bioactivity of the produced metabolites ahead of the time‐consuming isolation procedures. Herein, we showcase a structure‐agnostic bioactivity‐driven combinatorial biosynthesis workflow that introduces bioactivity assessment as a selection‐driving force to guide iterative combinatorial biosynthesis rounds towards enzyme combinations with increasing bioactivity. We apply this approach to produce triterpenoids with potent bioactivity against PTP1B, a promising molecular target for diabetes and cancer treatment. We demonstrate that the bioactivity‐guided workflow can expedite the combinatorial process by enabling the narrowing down of more than 1000 possible combinations to only five highly potent candidates. By focusing the isolation and structural elucidation effort on only these five strains, we reveal 20 structurally diverse triterpenoids, including four new compounds and a novel triterpenoid‐anthranilic acid hybrid, as potent PTP1B inhibitors. This workflow expedites hit identification by combinatorial biosynthesis and is applicable to many other types of bioactive natural products, therefore providing a strategy for accelerated drug discovery.

Structure‐Agnostic Bioactivity‐Driven Combinatorial Biosynthesis Reveals New Antidiabetic and Anticancer Triterpenoids

Although combinatorial biosynthesis can dramatically expand the chemical structures of bioactive natural products to identify molecules with improved characteristics, progress in this direction has been hampered by the difficulty in isolating and characterizing the numerous produced compounds. This challenge could be overcome with improved designs that enable the analysis of the bioactivity of the produced metabolites ahead of the time‐consuming isolation procedures. Herein, we showcase a structure‐agnostic bioactivity‐driven combinatorial biosynthesis workflow that introduces bioactivity assessment as a selection‐driving force to guide iterative combinatorial biosynthesis rounds towards enzyme combinations with increasing bioactivity. We apply this approach to produce triterpenoids with potent bioactivity against PTP1B, a promising molecular target for diabetes and cancer treatment. We demonstrate that the bioactivity‐guided workflow can expedite the combinatorial process by enabling the narrowing down of more than 1000 possible combinations to only five highly potent candidates. By focusing the isolation and structural elucidation effort on only these five strains, we reveal 20 structurally diverse triterpenoids, including four new compounds and a novel triterpenoid‐anthranilic acid hybrid, as potent PTP1B inhibitors. This workflow expedites hit identification by combinatorial biosynthesis and is applicable to many other types of bioactive natural products, therefore providing a strategy for accelerated drug discovery.

Coumarin derivatives as new anti-biofilm agents against Staphylococcus aureus

Staphylococcus aureus infections are the primary causes of morbidity, and mortality, particularly in immuno-compromised individuals. S. aureus associated infections are acquired from community, as well as hospital settings, and difficult to treat because of the emerging resistance against available antibiotics. One of the key factors of its resistance is the biofilm formation, which can be targeted to treat S. aureus-induced infections. Currently, there is no drug available that function by targeting the biofilm. This unmet need demands the discovery of drug candidates against S. aureus biofilm. The present study was designed to evaluate coumarin derivatives 1–21 against S. aureus biofilm. The 96-well plate crystal violet assay was employed for the quantification of biofilm. Results showed that the coumarin derivatives 2–4, 10, and 17 possess potent antibiofilm activity, with MBIC values between 25–100 μg/mL. The results were further confirmed through atomic force microscopy (AFM), scanning electron (SEM), and fluorescence microscopic studies. The quantitative RT-PCR analysis revealed the downregulation of biofilm associated genes, icaA and icaD. These coumarin derivatives were also found to be non-cytotoxic to fibroblasts. This study, therefore, identifies the antibiofilm potential of coumarin derivatives that will pave the way for further research on these derivatives.

Unraveling the role of lactate-related genes in myocardial infarction

BackgroundLactate is a crucial intermediary, facilitating communication between myocardial energy metabolism and microenvironmental regulation. The present study aimed to investigate the relationship between lactate-related genes (LRGs) and myocardial infarction (MI). MethodsA total of 23 LRGs exhibited differential expression between individuals with MI and healthy controls. Lasso regression analysis and validation with the GSE61144 dataset identified three hub genes: COX20, AGK, and PDHX. Single-gene GSEA of these genes revealed strong enrichment in pathways related to amino acid metabolism, cell cycle, and immune functions. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was utilized to validate the expression levels of the hub genes. ResultsImmune infiltration analysis revealed differences in CD4+ T and CD8+ T cells between the MI and control groups. Additionally, 67 candidate drugs targeting the three hub LRGs were identified, and a ceRNA network was constructed to explore the intricate interactions among these genes. ConclusionsThese findings enhance the understanding of MI and have potential therapeutic implications.

Literature Review: The Use of AI in Biomedical Applications

Artificial Intelligence (AI) has profoundly influenced the field of biomedicine, offering innovative solutions to complex challenges in medical diagnostics, drug discovery, and personalized treatment. This review examines the current state of AI applications in biomedicine, focusing on advancements in medical imaging, genomics, drug discovery, and personalized medicine. We discuss recent breakthroughs, explore the impact of AI on these areas, and address key challenges such as data quality, model interpretability, and integration into clinical practice. Finally, we propose future research directions to enhance the effectiveness and integration of AI in biomedicine. This review highlights the transformative potential of AI in improving healthcare outcomes and driving future innovations.

Synthesis of Aza‐S(VI) Fluorides and Primary Sulfonimidamides from Sulfinylamines

Aza‐S(VI) fluorides are crucial compounds in the synthesis of various S(VI) derivatives, which find broad applications in drug discovery. However, the synthesis of sulfonimidoyl and sulfondiimidoyl fluorides have been relatively underexplored, often requiring lengthy reaction sequences and/or the use of hazardous gaseous reagents. In this study, we present a rapid one‐pot method for producing sulfonimidoyl fluorides from sulfinylamines via a nucleophilic addition/electrophilic fluorination sequence. Similarly, sulfondiimidoyl fluorides can be synthesized using the same sequence, preceded by the formation of unsymmetrical sulfurdiimides readily generated in situ from sulfinylamines. Whereas isolation of sulfondiimidoyl fluorides was not feasible, they could be efficiently converted into primary sulfonimidamides by base hydrolysis in a one‐pot process. Furthermore, we explored the reactivity of aza‐S(VI) fluorides with various nucleophiles, demonstrating a versatile synthetic platform for the synthesis of aza‐S(VI) compounds..

The application of nanodiscs in membrane protein drug discovery & development and drug delivery

The phospholipid bilayer nanodiscs (LNDs), as a rapidly-developing tool in recent years, provide a natural bio-memebrane environment to maintain the native conformation and functions of membrane proteins as well as a versatile delivery vehicle for a variety of hydrophobic and hydrophilic drugs. We have seen unprecedented advantages of phospholipid bilayer nanodiscs in membrane protein structure characterization, biochemical and physiological studies of membrane proteins, membrane environment studies, drug discovery & development, and drug delivery. Many previous reviews have been mainly focused on the advantages of nanodiscs in membrane protein researches, but few have touched upon the importance and potential application of nanodiscs in pharmaceutical industries. This review will provide general description of the structural characteristics, advantages, classification, and applications of phospholipid nanodiscs, with particular focus on nanodisc-enabled membrane protein drug discovery & development as well as drug delivery.