Computer-aided drug design supporting sunscreen research: a showcase study using previously synthesized hybrid UV filter-antioxidant compounds.

CONTEXT: Although quantum mechanical calculations have proven effective in accurately predicting UV absorption and assessing the antioxidant potential of compounds, the utilization of computer-aided drug design (CADD) to support sustainable synthesis research of new sunscreen active ingredients remains an area with limited exploration. Furthermore, there are ongoing concerns about the safety and effectiveness of existing sunscreens. Therefore, it remains crucial to investigate photoprotection mechanisms and develop enhanced strategies for mitigating the harmful effects of UVR exposure, improving both the safety and efficacy of sunscreen products. A previous study conducted synthesis research on eight novel hybrid compounds (I-VIII) for use in sunscreen products by molecular hybridization of trans-resveratrol (RESV), avobenzone (AVO), and octinoxate (OMC). Herein, time-dependent density functional theory (TD-DFT) calculations performed in the gas phase on the isolated hybrid compounds (I-VIII) proved to reproduce the experimental UV absorption. Resveratrol-avobenzone structure-based hybrids (I-IV) present absorption maxima in the UVB range with slight differences between them, while resveratrol-OMC structure-based hybrids (V-VIII) showed main absorption in the UVA range. Among RESV-OMC hybrids, compounds V and VI exhibited higher UV absorption intensity, and compound VIII stood out for its broad-spectrum coverage in our simulations. Furthermore, both in silico and in vitro analyses revealed that compounds VII and VIII exhibited the highest antioxidant activity, with compound I emerging as the most reactive antioxidant within RESV-AVO hybrids. The study suggests a preference for the hydrogen atom transfer (HAT) mechanism over single-electron transfer followed by proton transfer (SET-PT) in the gas phase. With a strong focus on sustainability, this approach reduces costs and minimizes effluent production in synthesis research, promoting the eco-friendly development of new sunscreen active ingredients. METHODS: The SPARTAN'20 program was utilized for the geometry optimization and energy calculations of all compounds. Conformer distribution analysis was performed using the Merck molecular force field 94 (MMFF94), and geometry optimization was carried out using the parametric method 6 (PM6) followed by density functional theory (DFT/B3LYP/6-31G(d)). The antioxidant behavior of the hybrid compounds (I-VIII) was determined using the highest occupied molecular orbital (εHOMO) and the lowest unoccupied molecular orbital (εLUMO) energies, as well as the bond dissociation enthalpy (BDE), ionization potential (IP), and proton dissociation enthalpy (PDE) values, all calculated at the same level of structural optimization. TD-DFT study is carried out to calculate the excitation energy using the B3LYP functional with the 6-31G(d) basis set. The calculated transitions were convoluted with a Gaussian profile using the Gabedit program.

Genomics-informed captive breeding can reduce inbreeding depression and the genetic load in zoo populations.

Zoo populations of threatened species are a valuable resource for the restoration of wild populations. However, their small effective population size poses a risk to long-term viability, especially in species with high genetic load. Recent bioinformatic developments can identify harmful genetic variants in genome data. Here, we advance this approach, analysing the genetic load in the threatened pink pigeon (Nesoenas mayeri). We lifted the mutation-impact scores that had been calculated for the chicken (Gallus gallus) to estimate the genetic load in six pink pigeons. Additionally, we perform in silico crossings to predict the genetic load and realized load of potential offspring. We thus identify the optimal mate pairs that are theoretically expected to produce offspring with the least inbreeding depression. We use computer simulations to show how genomics-informed conservation can reduce the genetic load whilst reducing the loss of genome-wide diversity. Genomics-informed management is likely to become instrumental in maintaining the long-term viability of zoo populations.

The Prediction of LptA and LptC Protein-Protein Interactions and Virtual Screening for Potential Inhibitors.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein-protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein-protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research.

Effects of reference population size and structure on genomic prediction of maternal traits in two pig lines using whole-genome sequence-, high-density- and combined annotation-dependent depletion genotypes.

The aim of this study was to investigate the reference population size required to obtain substantial prediction accuracy within- and across-lines and the effect of using a multi-line reference population for genomic predictions of maternal traits in pigs. The data consisted of two nucleus pig populations, one pure-bred Landrace (L) and one Synthetic (S) Yorkshire/Large White line. All animals were genotyped with up to 30 K animals in each line, and all had records on maternal traits. Prediction accuracy was tested with three different marker data sets: High-density SNP (HD), whole genome sequence (WGS), and markers derived from WGS based on pig combined annotation dependent depletion-score (pCADD). Also, two different genomic prediction methods (GBLUP and Bayes GC) were compared for four maternal traits; total number piglets born (TNB), total number of stillborn piglets (STB), Shoulder Lesion Score and Body Condition Score. The main results from this study showed that a reference population of 3 K-6 K animals for within-line prediction generally was sufficient to achieve high prediction accuracy. However, when the number of animals in the reference population was increased to 30 K, the prediction accuracy significantly increased for the traits TNB and STB. For multi-line prediction accuracy, the accuracy was most dependent on the number of within-line animals in the reference data. The S-line provided a generally higher prediction accuracy compared to the L-line. Using pCADD scores to reduce the number of markers from WGS data in combination with the GBLUP method generally reduced prediction accuracies relative to GBLUP using HD genotypes. The BayesGC method benefited from a large reference population and was less dependent on the different genotype marker datasets to achieve a high prediction accuracy.

Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis.

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.

Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery.

Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.

EGFR Kinase Inhibiting Amino-enones for Breast Cancer; CADD Approach.

BACKGROUND: The Computer-Aided Drug Discovery (CADD) approach was used to develop a few Epidermal Growth Factor Receptor (EGFR) kinase inhibitors. EGFR kinase expression is highly associated with genomic instability, higher proliferation, lower hormone receptor levels, and HER2 over-expression. It is more common in breast cancer. Thus, EGFR Kinase is one of the main targets in discovering new cancer medicine. OBJECTIVE: To computationally validate some amides substituted ß-amino enones as EGFR inhibitors and to carry out associated in vitro anticancer agents. METHODS: We used tools such as molecular docking, MD simulations, DFT calculations, and ADMET predictions in silico to establish a preliminary SAR. In vitro, we used BT474 (ER+HER2+) and MCF-7 (ER-HER2) cell lines along with normal breast cell epithelial cells (MFC-10a) for anticancer studies and EGFR kinase inhibition assay studies. As the Reactive Oxygen Species (ROS) plays the main role in cancer development, we also analyzed the antioxidant potentials of these compounds. RESULTS: Among the family of eleven amides substituted (Z)-ß-amino enones (5a-k), compounds 5b, 5c, 5g, and 5h showed valuable in silico and in vitro bio-activity. Remarkably, the in-silico results almost coincided with in vitro study results. CONCLUSION: We recommend compounds 5b, 5c, 5g, and 5h for pre-clinical and clinical evaluation to establish them as future cancer therapeutics.

Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PLpro) Inhibitor.

The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its companion disease, COVID-19, has reminded us of the importance of basic coronaviral research. In this study, a comprehensive approach using molecular docking, in vitro assays, and molecular dynamics simulations was applied to identify potential inhibitors for SARS-CoV-2 papain-like protease (PLpro), a key and underexplored viral enzyme target. A focused protease inhibitor library was initially created and molecular docking was performed using CmDock software (v0.2.0), resulting in the selection of hit compounds for in vitro testing on the isolated enzyme. Among them, compound 372 exhibited promising inhibitory properties against PLpro, with an IC50 value of 82 ± 34 µM. The compound also displayed a new triazolopyrimidinyl scaffold not yet represented within protease inhibitors. Molecular dynamics simulations demonstrated the favorable binding properties of compound 372. Structural analysis highlighted its key interactions with PLpro, and we stress its potential for further optimization. Moreover, besides compound 372 as a candidate for PLpro inhibitor development, this study elaborates on the PLpro binding site dynamics and provides a valuable contribution for further efforts in pan-coronaviral PLpro inhibitor development.

An interdisciplinary course on computer-aided drug discovery to broaden student participation in original scientific research.

We present a new highly interdisciplinary project-based course in computer aided drug discovery (CADD). This course was developed in response to a call for alternative pedagogical approaches during the COVID-19 pandemic, which caused the cancellation of a face-to-face summer research program sponsored by the Louisiana Biomedical Research Network (LBRN). The course integrates guided research and educational experiences for chemistry, biology, and computer science students. We implement research-based methods with publicly available tools in bioinformatics and molecular modeling to identify and prioritize promising antiviral drug candidates for COVID-19. The purpose of this course is three-fold: I. Implement an active learning and inclusive pedagogy that fosters student engagement and research mindset; II. Develop student interdisciplinary research skills that are highly beneficial in a broader scientific context; III. Demonstrate that pedagogical shifts (initially incurred during the COVID-19 pandemic) can furnish longer-term instructional benefits. The course, which has now been successfully taught a total of five times, incorporates four modules, including lectures/discussions, live demos, inquiry-based assignments, and science communication.

The discovery of novel and potent indazole NLRP3 inhibitors enabled by DNA-encoded library screening.

NLRP3 is an intracellular sensor protein that detects a broad range of danger signals and environmental insults. Its activation results in a protective pro-inflammatory response designed to impair pathogens and repair tissue damage via the formation of the NLRP3 inflammasome. Assembly of the NLRP3 inflammasome leads to caspase 1-dependent secretory release of the pro-inflammatory cytokines IL-1ß and IL-18 as well as to gasdermin d-mediated pyroptotic cell death. Herein, we describe the discovery of a novel indazole series of high affinity, reversible inhibitors of NLRP3 activation through screening of DNA-encoded libraries and the potent lead compound 3 (BAL-0028, IC50 = 25 nM) that was identified directly from the screen. SPR studies showed that compound 3 binds tightly (KD range 104-123 nM) to the NACHT domain of NLRP3. A CADD analysis of the interaction of compound 3 with the NLRP3 NACHT domain proposes a binding site that is distinct from those of ADP and MCC950 and includes specific site interactions. We anticipate that compound 3 (BAL-0028) and other members of this novel indazole class of neutral inhibitors will demonstrate significantly different physical, biochemical, and biological properties compared to NLRP3 inhibitors previously identified.

Receptor-based pharmacophore modeling, molecular docking, synthesis and biological evaluation of novel VEGFR-2, FGFR-1, and BRAF multi-kinase inhibitors.

A receptor-based pharmacophore model describing the binding features required for the multi-kinase inhibition of the target kinases (VEGFR-2, FGFR-1, and BRAF) were constructed and validated. It showed a good overall quality in discriminating between the active and the inactive in a compiled test set compounds with F1 score of 0.502 and Mathew's correlation coefficient of 0.513. It described the ligand binding to the hinge region Cys or Ala, the glutamate residue of the Glu-Lys αC helix conserved pair, the DFG motif Asp at the activation loop, and the allosteric back pocket next to the ATP binding site. Moreover, excluded volumes were used to define the steric extent of the binding sites. The application of the developed pharmacophore model in virtual screening of an in-house scaffold dataset resulted in the identification of a benzimidazole-based scaffold as a promising hit within the dataset. Compounds 8a-u were designed through structural optimization of the hit benzimidazole-based scaffold through (un)substituted aryl substitution on 2 and 5 positions of the benzimidazole ring. Molecular docking simulations and ADME properties predictions confirmed the promising characteristics of the designed compounds in terms of binding affinity and pharmacokinetic properties, respectively. The designed compounds 8a-u were synthesized, and they demonstrated moderate to potent VEGFR-2 inhibitory activity at 10 µM. Compound 8u exhibited a potent inhibitory activity against the target kinases (VEGFR-2, FGFR-1, and BRAF) with IC50 values of 0.93, 3.74, 0.25 µM, respectively. The benzimidazole derivatives 8a-u were all selected by the NCI (USA) to conduct their anti-proliferation screening. Compounds 8a and 8d resulted in a potent mean growth inhibition % (GI%) of 97.73% and 92.51%, respectively. Whereas compounds 8h, 8j, 8k, 8o, 8q, 8r, and 8u showed a mean GI% > 100% (lethal effect). The most potent compounds on the NCI panel of 60 different cancer cell lines were progressed further to NCI five-dose testing. The benzimidazole derivatives 8a, 8d, 8h, 8j, 8k, 8o, 8q, 8r and 8u exhibited potent anticancer activity on the tested cell lines reaching sub-micromolar range. Moreover, 8u was found to induce cell cycle arrest of MCF-7 cell line at the G2/M phase and accumulating cells at the sub-G1 phase as a result of cell apoptosis.

A comprehensive review of discovery and development of drugs discovered from 2020-2022.

To fully evaluate and define the new drug molecule for its pharmacological characteristics and toxicity profile, pre-clinical and clinical studies are conducted as part of the drug research and development process. The average time required for all drug development processes to finish various regulatory evaluations ranges from 11.4 to 13.5 years, and the expense of drug development is rising quickly. The development in the discovery of newer novel treatments is, however, largely due to the growing need for new medications. Methods to identify Hits and discovery of lead compounds along with pre-clinical studies have advanced, and one example is the introduction of computer-aided drug design (CADD), which has greatly shortened the time needed for the drug to go through the drug discovery phases. The pharmaceutical industry will hopefully be able to address the present and future issues and will continue to produce novel molecular entities (NMEs) to satisfy the expanding unmet medical requirements of the patients as the success rate of the drug development processes is increasing. Several heterocyclic moieties have been developed and tested against many targets and proved to be very effective. In-depth discussion of the drug design approaches of newly found drugs from 2020 to 2022, including their pharmacokinetic and pharmacodynamic profiles and in-vitro and in-vivo assessments, is the main goal of this review. Considering the many stages these drugs are going through in their clinical trials, this investigation is especially pertinent. It should be noted that synthetic strategies are not discussed in this review; instead, they will be in a future publication.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials.

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Identification, characterization, and CADD analysis of Plasmodium DMAP1 reveals it as a potential molecular target for new anti-malarial discovery.

Developing drug resistance in the malaria parasite is a reason for apprehension compelling the scientific community to focus on identifying new molecular targets that can be exploited for developing new anti-malarial compounds. Despite the availability of the Plasmodium genome, many protein-coding genes in Plasmodium are still not characterized or very less information is available about their functions. DMAP1 protein is known to be essential for growth and plays an important role in maintaining genomic integrity and transcriptional repression in vertebrate organisms. In this study, we have identified a homolog of DMAP1 in P. falciparum. Our sequence and structural analysis showed that although PfDMAP1 possesses a conserved SANT domain, parasite protein displays significant structural dissimilarities from human homolog at full-length protein level as well as within its SANT domain. PPIN analysis of PfDMAP1 revealed it to be vital for parasite and virtual High-throughput screening of various pharmacophore libraries using BIOVIA platform-identified compounds that pass ADMET profiling and showed specific binding with PfDMAP1. Based on MD simulations and protein-ligand interaction studies two best hits were identified that could be novel potent inhibitors of PfDMAP1 protein.Communicated by Ramaswamy H. Sarma.

Computer aided drug discovery (CADD) of a thieno[2,3-d]pyrimidine derivative as a new EGFR inhibitor targeting the ribose pocket.

Depending on the pharmacophoric characteristics of EGFR inhibitors, a new thieno[2,3-d]pyrimidine derivative has been developed. Firstly, the potential inhibitory effect of the designed compound against EGFR has been proven by docking experiments that showed correct binding modes and excellent binding energies of -98.44 and -88.00 kcal/mol, against EGFR wild-type and mutant type, respectively. Furthermore, MD simulations studies confirmed the precise energetic, conformational, and dynamic alterations that occurred after binding to EGFR. The correct binding was also confirmed by essential dynamics studies. To further investigate the general drug-like properties of the developed candidate, in silico ADME and toxicity studies have also been carried out. The thieno[2,3-d]pyrimidine derivative was synthesized following the earlier promising findings. Fascinatingly, the synthesized compound (4) showed promising inhibitory effects against EGFRWT and EGFRT790M with IC50 values of 25.8 and 182.3 nM, respectively. Also, it exhibited anticancer potentialities against A549 and MCF-7cell lines with IC50 values of 13.06 and 20.13 µM, respectively. Interestingly, these strong activities were combined with selectivity indices of 2.8 and 1.8 against the two cancer cell lines, respectively. Further investigations indicated the ability of compound 4 to arrest the cancer cells' growth at the G2/M phase and to increase early and late apoptosis percentages from 2.52% and 2.80 to 17.99% and 16.72%, respectively. Additionally, it was observed that compound 4 markedly increased the levels of caspase-3 and caspase-9 by 4 and 3-fold compared to the control cells. Moreover, it up-regulated the level of BAX by 3-fold and down-regulated the level of Bcl-2 by 3-fold affording a BAX/Bcl-2 ratio of 9.Communicated by Ramaswamy H. Sarma.

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.

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.

Exploring Monkeypox: prospects for therapeutics through computational-aided drug discovery.

Monkeypox virus (MPXV) has emerged as a significant public health concern due to its potential for human transmission and its severe clinical manifestations. This review synthesizes findings from peer-reviewed articles spanning the last two decades, shedding light on diverse aspects of MPXV research. The exploration commences with an analysis of transmission dynamics, including zoonotic and human-to-human transmission, and potential reservoir hosts. Detailed insights into viral replication mechanisms illuminate its influence on disease progression and pathogenicity. Understanding the genomic and virion structure of MPXV is pivotal for targeted interventions. Genomic characteristics contributing to virulence are examined, alongside recent advancements in virion structure elucidation through cutting-edge imaging techniques. Emphasizing combat strategies, the review lists potential protein targets within the MPXV lifecycle for computer-aided drug design (CADD). The role of protein-ligand interactions and molecular docking simulations in identifying potential drug candidates is highlighted. Despite the absence of approved MPXV medications, the review outlines updates on ongoing small molecules and vaccine development efforts, spanning traditional and innovative platforms. The evolving landscape of computational drug research for MPXV is explored, encompassing advanced algorithms, machine learning, and high-performance computing. In conclusion, this review offers a holistic perspective on MPXV research by integrating insights spanning transmission dynamics to drug design. Equipping researchers with multifaceted understanding underscore the importance of innovative methodologies and interdisciplinary collaborations in addressing MPXV's challenges as research advances.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing.

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.