Identification Of Drug Candidates Research Articles

Neutral Red Labeling: A Novel Vital Staining Method for Investigating Central and Peripheral Nervous System Lesions.

Multiple sclerosis, neuromyelitis optica, Guillain-Barré syndrome and chronic inflammatory demyelinating polyradiculoneuropathy are representative demyelinating diseases of the central and peripheral nervous system. Remyelination by myelin forming cells is important for functional recovery from the neurological deficits caused in the demyelinating diseases. Lysophosphatidylcholine-induced demyelination in mice is commonly used to identify and study the molecular pathways of demyelination and remyelination. However, detection of focally demyelinated lesions is difficult and usually requires sectioning of demyelinated lesions in tissues for microscopic analysis. In this review, we describe the development and application of a novel vital staining method for labeling demyelinated lesions using intraperitoneal injection of neutral red (NR) dye. NR labeling reduces the time and effort required to search for demyelinated lesions in tissues, and facilitates electron microscopic analysis of myelin structures. NR labeling also has the potential to contribute to the elucidation of pathologies in the central and peripheral nervous system and assist with identification of drug candidates that promote remyelination.

A General Three-Component Alkyl Petasis Boron-Mannich Reaction.

Aryl amines are one of the most common moieties in biologically active molecules, and approximately 37% of drug candidates contain aromatic amines. Recent advancements in medicinal chemistry, coined "escaping from flatland", have led to a greater focus on accessing highly functionalized C (sp3)-rich amines to improve the physicochemical and pharmacokinetic properties of compounds. This article presents a modular and operationally straightforward three-component alkyl Petasis boron-Mannich (APBM) reaction that utilizes ubiquitous starting materials, including amines, aldehydes, and alkyl boronates. By adaptation of this transformation to high-throughput experimentation (HTE), it offers rapid access to an array of diverse C(sp3)-rich complex amines, amenable for rapid identification of drug candidates.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases.

Gynecological diseases are triggered by aberrant molecular pathways that alter gene expression, hormonal balance, and cellular signaling pathways, which may lead to long-term physiological consequences. This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database. Five highly preserved modules were observed across the EM (GSE51981), OC (GSE63885), CC (GSE63514), and EC (GSE17025) datasets. The functional annotation and pathway enrichment analysis revealed that the highly preserved modules were heavily involved in several inflammatory pathways that are associated with transcription dysregulation, such as NF-kB signaling, JAK-STAT signaling, MAPK-ERK signaling, and mTOR signaling pathways. Furthermore, the results also include pathways that are relevant in gynecological disease prognosis through viral infections. Mutations in the ESR1 gene that encodes for ERα, which were shown to also affect signaling pathways involved in inflammation, further indicate its importance in gynecological disease prognosis. Potential drugs were screened through the Drug Repurposing Encyclopedia (DRE) based on the up-and downregulated hub genes, wherein a bacterial ribosomal subunit inhibitor and a benzodiazepine receptor agonist were the top candidates. Other drug candidates include a dihydrofolate reductase inhibitor, glucocorticoid receptor agonists, cholinergic receptor agonists, selective serotonin reuptake inhibitors, sterol demethylase inhibitors, a bacterial antifolate, and serotonin receptor antagonist drugs which have known anti-inflammatory effects, demonstrating that the gene network highlights specific inflammatory pathways as a therapeutic avenue in designing drug candidates for gynecological diseases.

Network pharmacology speaking to ethnopharmacology: new data on an ancient remedy

Network pharmacology as a “green approach”, predicting metabolite behaviours chemically and biologically and guid¬ing biological experimental design, is a new strategy aiming to uncover the mechanism of action of natural products as drug candidates. It provides a powerful way to identify novel mechanisms of natural products with potential thera¬peutic effects. This approach has emerged as a powerful tool to overcome the limitations of traditional methods, such as the ability to predict the adverse effects of a drug and the likelihood of failure during clinical trials, by applying systems biology principles to the field of pharmacology. This method combines the multi-omics dataset, computer modeling, and chemical biology so as to reveal pharmaceutical actions and guide drug discovery. Therefore, computer-aided drug design combined with network pharmacology can be viewed as a novel in silico screening ap¬proach to drug discovery, by utilising chemoinformatics, bioinformatics, structure biology, and chemical biology. This strategy includes target-based virtual screening - molecular docking, ligand similarity-based virtual screening, and inverse screening (Inver-dock), providing a powerful tool for target identification of drug candidates, multitarget dis¬covery, and natural bioactive product profiling. It can also be used for selectivity profiling of drugs, drug repositioning, safety profiling, and metabolism profiling prediction (ADMET).

Delineating the impact of N21D mutation on the conformational preferences and structural transitions in human islet amyloid polypeptide

The amyloid deposits of human islet amyloid polypeptide (hIAPP) in the pancreatic β-cells are linked to a chronic metabolic disease known as type 2 diabetes (T2D). Recent studies reported that N21D mutation in hIAPP inhibited the formation of fibrillar aggregates, however, the underlying molecular mechanism of fibril inhibition in the N21D mutant remains largely unclear. Thus, molecular dynamics (MD) simulations have been employed to illuminate the effect of N21D mutation on the conformational dynamics and structural transformations in hIAPP at the atomistic level. Notably, the structural stability of WT-hIAPP (wild type-hIAPP) was enhanced upon N21D mutation. Interestingly, the sampling of helical conformation in hIAPP was significantly increased from 12.60 to 19.80% with a concomitant decrease in β-sheet from 7.40 to 1.20% upon N21D mutation that corroborates with the circular dichroism (CD). The dihedral principal component analysis (dPCA) depicted a notable reduction in the conformational phase space of [N21D]hIAPP as compared to WT-hIAPP, which highlights the prevention of conformational transitions and enhanced structural stability. Remarkably, a complete loss in the β-sheet content was noted in the conformations extracted from the free energy landscape of [N21D]hIAPP as compared to 24–25% sampling of β-sheets in minimum energy conformations of WT-hIAPP consistent with the transmission electron microscopy (TEM) and thioflavin T (ThT) fluorescence assay. The present study provided key insights into the N21D mutation-induced conformational changes in hIAPP and highlighted that Asn21 plays a key role in hIAPP aggregation. The insights from this work will be beneficial in the design of drug candidates by selectively targeting Asn21 of hIAPP which could interfere with its aggregation and associated cytotoxicity.

Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index.

Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction. In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment. Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature. Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.

Unlocking Opportunities for Mycobacterium leprae and Mycobacterium ulcerans.

In the recent decade, scientific communities have toiled to tackle the emerging burden of drug-resistant tuberculosis (DR-TB) and rapidly growing opportunistic nontuberculous mycobacteria (NTM). Among these, two neglected mycobacteria species of the Acinetobacter family, Mycobacterium leprae and Mycobacterium ulcerans, are the etiological agents of leprosy and Buruli ulcer infections, respectively, and fall under the broad umbrella of neglected tropical diseases (NTDs). Unfortunately, lackluster drug discovery efforts have been made against these pathogenic bacteria in the recent decade, resulting in the discovery of only a few countable hits and majorly repurposing anti-TB drug candidates such as telacebec (Q203), P218, and TB47 for current therapeutic interventions. Major ignorance in drug candidate identification might aggravate the dramatic consequences of rapidly spreading mycobacterial NTDs in the coming days. Therefore, this Review focuses on an up-to-date account of drug discovery efforts targeting selected druggable targets from both bacilli, including the accompanying challenges that have been identified and are responsible for the slow drug discovery. Furthermore, a succinct discussion of the all-new possibilities that could be alternative solutions to mitigate the neglected mycobacterial NTD burden and subsequently accelerate the drug discovery effort is also included. We anticipate that the state-of-the-art strategies discussed here may attract major attention from the scientific community to navigate and expand the roadmap for the discovery of next-generation therapeutics against these NTDs.

Exploring NCATS in-house biomedical data for evidence-based drug repurposing.

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents

In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid advancement of computer-aided discovery techniques and the emergence of biosimilar agents. This dynamic interplay between scientific innovation and technological prowess has expedited the drug discovery process and paved the way for more targeted, effective, and personalized treatment approaches. This review investigates the transformative computer-aided discovery techniques for biosimilar agents in reshaping drug design. It examines how computational methods expedite drug candidate identification and explores the rise of cost-effective biosimilars as alternatives to biologics. Through this analysis, this study highlights the potential of these innovations to enhance the efficiency and accessibility of pharmaceutical development. It represents a pioneering effort to examine how computer-aided discovery is revolutionizing biosimilar agent development, exploring its applications, challenges, and prospects.

Identification of drug candidates targeting monocyte reprogramming in people living with HIV.

People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Deciphering the Inhibitory Mechanism of Naphthoquinone-Dopamine on the Aggregation of Tau Core Fragments PHF6* and PHF6.

The formation of neurofibrillary tangles by abnormal aggregation of tau protein is considered to be an important pathological characteristic of tauopathies, including Alzheimer's disease and chronic traumatic encephalopathy. Two hexapeptides 275VQIINK280 and 306VQIVYK311 in the microtubule binding region, named PHF6* and PHF6, are known to be aggregation-prone and responsible for tau fibrillization. Previous experiments reported that naphthoquinone-dopamine (NQDA) could effectively inhibit the aggregation of PHF6* and PHF6 and disrupt the fibrillar aggregates into nontoxic species, displaying a dual effect on the amyloid aggregation. However, the underlying molecular mechanism remains mostly elusive. Herein, we performed all-atom molecular dynamics (MD) simulations for 114 μs in total to systematically investigate the impacts of NQDA on the oligomerization of PHF6* and PHF6. The conformational ensembles of PHF6* and PHF6 peptides generated by replica exchange MD simulations show that NQDA could effectively prevent the hydrogen bond formation, reduce the ability of peptides to self-assemble into long β-strand and large β-sheets, and induce peptides to form a loosely packed and coil-rich oligomer. The interaction analysis shows that the binding of NQDA to PHF6* is mainly through hydrophobic interactions with residue I277 and hydrogen bonding interactions with Q276; for the PHF6 peptides, NQDA displays a strong π-π stacking interaction with residue Y310, thus impeding the Y310-Y310 π-π stacking and I308-Y310 CH-π interactions. The DA group of NQDA displays a stronger cation-π interaction than the NQ group, while the NQ group exhibits a stronger π-π stacking interaction. MD simulations demonstrate that NQDA prevents the conformational conversion to β-sheet-rich aggregates and displays an inhibitory effect on the oligomerization dynamics of PHF6* and PHF6. Our results provide a complete picture of inhibitory mechanisms of NQDA on PHF6* and PHF6 oligomerization, which may pave the way for designing drug candidates for the treatment of tauopathies.

Network pharmacology and molecular docking-based identification of drug candidates and key targets of Allium sativum for colorectal cancer treatment

Colorectal cancer (CRC) is a type of cancer with high morbidity and mortality in several developing and developed countries of the world. Its mortality and morbidity are predicted to increase over the next decade, hence, efforts aimed at combating it have remained unabated. In the context of its treatment, the use of chemotherapeutics is often limited by challenges including cost-ineffectiveness, side effects, and drug resistance. Hence, medicinal plants are actively being explored for alternatives. In this study, Allium sativum (A. sativum) was explored for the discovery of key compounds that are worthy of exploration in the context of CRC treatment and the potential mechanism of its anti-CRC effects. The bioactive compounds of A. sativum were retrieved and subjected to drug-likeness and pharmacokinetics properties evaluation, the putative targets of compounds with admirable properties were predicted using PharmMapper while the targets of CRC were retrieved from GeneCards. The interactions between the targets common to both were retrieved from the String database while Cytoscape software was used to visualize and analyze the interactions. Gene set enrichment analysis (GSEA) study revealed the biological processes and pathways A. sativum could potentially restore in CRC. These analyses revealed the key targets via which A. sativum compounds exert their anti-CRC properties, while molecular docking studies of the key compounds against the key targets revealed beta-sitosterol and alpha-bisabolene as the compounds with the highest binding affinity for the key targets. Ultimately, further experimental studies are needed to validate the findings of this study. Communicated by Ramaswamy H. Sarma

Myricetin served as antagonist for negatively regulate MRGPRX2 mediated pseudo-allergic reactions through CD300f/SHP1/SHP2 phosphorylation.

Mas-related G protein-coupled receptor X2 (MRGPRX2) plays a vital role in mast cells (MCs) degranulation and pseudo-allergic reactions. Leukocyte mono-immunoglobulin-like receptor 3 (CD300f) can negatively regulate MCs degranulation. Identification of drug candidates which target CD300f represents a promising prospect in drug development. Myricetin is widely distributed in plants and has been reported to inhibit allergic reactions in OVA-induced murine models. This study aims to determine whether myricetin can activate CD300f to arrest MCs degranulation mediated by MRGPRX2. Myricetin inhibited the allergic mediator and cytokine release triggered by MRGPRX2 in vivo and in vitro. Under C48/80 stimulation, the release of β-hexosaminidase, TNF-α, IL-8 and MCP-1 in CD300f knockdown in LAD2 cells was significantly increased compared with NC-LAD2 cells. Myricetin displayed good structural affinity (KD=7.21×10-5) with CD300f by SPR. Molecular docking results showed that hydrogen bonds were formed between myricetin and CD300f, indicating high binding ability (5.6653). Myricetin can upregulate the phosphorylation of SHP-1 and SHP-2 and dephosphorylation in the MRGPRX2 signaling pathway, involving PLCγ1, AKT, P38, and ERK1/2. In the present study, myricetin is identified as an exogenous ligand for CD300f, which negatively regulates MRGPRX2-mediated MCs activation via CD300f to inhibit MCs degranulation and pseudo-allergic reactions.

Drug repurposing in autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease is characterized by progressive kidney cyst formation that leads to kidney failure. Tolvaptan, a vasopressin 2 receptor antagonist, is the only drug approved to treat patients with autosomal dominant polycystic kidney disease who have rapid disease progression. The use of tolvaptan is limited by reduced tolerability from aquaretic effects and potential hepatotoxicity. Thus, the search for more effective drugs to slow down the progression of autosomal dominant polycystic kidney disease is urgent and challenging. Drug repurposing is a strategy for identifying new clinical indications for approved or investigational medications. Drug repurposing is increasingly becoming an attractive proposition because of its cost-efficiency and time-efficiency and known pharmacokinetic and safety profiles. In this review, we focus on the repurposing approaches to identify suitable drug candidates to treat autosomal dominant polycystic kidney disease and prioritization and implementation of candidates with high probability of success. Identification of drug candidates through understanding of disease pathogenesis and signaling pathways is highlighted.

Fragment expansion with NUDELs - poised DNA-encoded libraries.

Optimisation of the affinity of lead compounds is a critical challenge in the identification of drug candidates and chemical probes and is a process that takes many years. Fragment-based drug discovery has become established as one of the methods of choice for drug discovery starting with small, low affinity compounds. Due to their low affinity, the evolution of fragments to desirable levels of affinity is often a key challenge. The accepted best method for increasing the potency of fragments is by iterative fragment growing, which can be very time consuming and complex. Here, we introduce a paradigm for fragment hit optimisation using poised DNA-encoded chemical libraries (DELs). The synthesis of a poised DEL, a partially constructed library that retains a reactive handle, allows the coupling of any active fragment for a specific target protein, allowing rapid discovery of potent ligands. This is illustrated for bromodomain-containing protein 4 (BRD4), in which a weakly binding fragment was coupled to a 42-member poised DEL via Suzuki-Miyaura cross coupling resulting in the identification of an inhibitor with 51 nM affinity in a single step, representing an increase in potency of several orders of magnitude from an original fragment. The potency of the compound was shown to arise from the synergistic combination of substructures, which would have been very difficult to discover by any other method and was rationalised by X-ray crystallography. The compound showed attractive lead-like properties suitable for further optimisation and demonstrated BRD4-dependent cellular pharmacology. This work demonstrates the power of poised DELs to rapidly optimise fragments, representing an attractive generic approach to drug discovery.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Network-based drug repurposing for potential stroke therapy

Stroke is the leading cause of death and disability worldwide, with a growing number of incidences in developing countries. However, there are currently few medical therapies for this disease. Emerged as an effective drug discovery strategy, drug repurposing which owns lower cost and shorter time, is able to identify new indications from existing drugs. In this study, we aimed at identifying potential drug candidates for stroke via computationally repurposing approved drugs from Drugbank database. We first developed a drug-target network of approved drugs, employed network-based approach to repurpose these drugs, and altogether identified 185 drug candidates for stroke. To validate the prediction accuracy of our network-based approach, we next systematically searched for previous literature, and found 68 out of 185 drug candidates (36.8 %) exerted therapeutic effects on stroke. We further selected several potential drug candidates with confirmed neuroprotective effects for testing their anti-stroke activity. Six drugs, including cinnarizine, orphenadrine, phenelzine, ketotifen, diclofenac and omeprazole, have exhibited good activity on oxygen-glucose deprivation/reoxygenation (OGD/R) induced BV2 cells. Finally, we showcased the anti-stroke mechanism of actions of cinnarizine and phenelzine via western blot and Olink inflammation panel. Experimental results revealed that they both played anti-stroke effects in the OGD/R induced BV2 cells via inhibiting the expressions of IL-6 and COX-2. In summary, this study provides efficient network-based methodologies for in silico identification of drug candidates toward stroke.

Integration of cancer stemness and neoantigen load to predict responsiveness to anti-PD1/PDL1 therapy.

Background: Anti-programmed cell death 1/programmed cell death ligand 1 (PD1/PDL1) therapy is an important part of comprehensive cancer therapy. However, many patients suffer from non-response to therapy. Tumor neoantigen burden (TNB) and cancer stemness play essential roles in the responsiveness to therapy. Therefore, the identification of drug candidates for anti-PD1/PDL1 therapy remains an unmet need. Methods: Three anti-PD1/PDL1 therapy cohorts were obtained from GEO database and published literatures. Cancer immune characteristics were analyzed using CIBERSORTX, GSVA, and ESTIMATE. WGCNA was employed to identify the gene modules correlated with cancer TNB and stemness. A machine-learning method was used to construct the immunotherapy resistance score (TSIRS). Pharmacogenomic analysis was conducted to explore the potential alternative drugs for anti-PD1/PDL1 therapy resistant patients. CCK-8 assay, EdU assay and wound healing assay were used to validate the effect of the predicted drug on cancer cells. Results: The therapy response and non-response cancer groups have different microenvironment features. TSIRS was developed based on tumor neoantigen and stemness. TSIRS can effectively predict the outcomes of patients with anti-PD1/PDL1 therapy in training, validation and meta cohorts. Meanwhile, TSIRS can reflect the characteristics of tumor microenvironment during anti-PD1/PDL1 therapy. PF-4708671 is identified as a potential alternative drug for patients with resistance to anti-PD1/PDL1 therapy. It possesses significant inhibitive effect on the proliferation and migration of BGC-823 cells. Conclusion: TSIRS is an effective tool in the identification of candidate patients who will be benefit from anti-PD1/PDL1 therapy. Small molecule drug PF-4708671 has the potential to be used in anti-PD1/PDL1 therapy resistant patients.

Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.

The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients. COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification. In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients. In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.