Systems Pharmacology Approach Research Articles

Molecular interactions between metformin and D-limonene inhibit proliferation and promote apoptosis in breast and liver cancer cells

BackgroundCancer is a fatal disease that severely affects humans. Designing new anticancer strategies and understanding the mechanism of action of anticancer agents is imperative.Hypothesis/PurposeIn this study, we evaluated the utility of metformin and D-limonene, alone or in combination, as potential anticancer therapeutics using the human liver and breast cancer cell lines HepG2 and MCF-7.Study designAn integrated systems pharmacology approach is presented for illustrating the molecular interactions between metformin and D-limonene.MethodsWe applied a systems-based analysis to introduce a drug–target–pathway network that clarifies different mechanisms of treatment. The combination treatment of metformin and D-limonene induced apoptosis in both cell lines compared with single drug treatments, as indicated by flow cytometric and gene expression analysis.ResultsThe mRNA expression of Bax and P53 genes were significantly upregulated while Bcl-2, iNOS, and Cox-2 were significantly downregulated in all treatment groups compared with normal cells. The percentages of late apoptotic HepG2 and MCF-7 cells were higher in all treatment groups, particularly in the combination treatment group. Calculations for the combination index (CI) revealed a synergistic effect between both drugs for HepG2 cells (CI = 0.14) and MCF-7 cells (CI = 0.22).ConclusionOur data show that metformin, D-limonene, and their combinations exerted significant antitumor effects on the cancer cell lines by inducing apoptosis and modulating the expression of apoptotic genes.

Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects.

Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug-target-adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data-driven biological plausibility assessment in the context of challenging post-marketing DDI surveillance.

Distinguishing Inducible and Non-Inducible Resistance to Colistin in Pseudomonas aeruginosa by Quantitative and Systems Pharmacology Modeling at Low and Standard Inocula

Colistin is a polymyxin and peptide antibiotic that can yield rapid bacterial killing, but also leads to resistance emergence. We aimed to develop a novel experimental and Quantitative and Systems Pharmacology approach to distinguish between inducible and non-inducible resistance. Viable count profiles for the total and less susceptible populations of Pseudomonas aeruginosa ATCC 27853 from static and dynamic in vitro infection models were simultaneously modeled. We studied low and normal initial inocula to distinguish between inducible and non-inducible resistance. A novel cutoff filter approach allowed us to describe the eradication and inter-conversion of bacterial populations. At all inocula, 4.84 mg/L of colistin (sulfate) yielded ≥4 log10 killing, followed by >4 log10 regrowth. A pre-existing, less susceptible population was present at standard but not at low inocula. Formation of a non-pre-existing, less susceptible population was most pronounced at intermediate colistin (sulfate) concentrations (0.9 to 5 mg/L). Both less susceptible populations inter-converted with the susceptible population. Simultaneously modeling of the total and less susceptible populations at low and standard inocula enabled us to identify the de novo formation of an inducible, less susceptible population. Inducible resistance at intermediate colistin concentrations highlights the importance of rapidly achieving efficacious polymyxin concentrations by front-loaded dosage regimens.

Exploring Molecular Mechanisms of Traditional Persian Medicine-based Herbal Remedies for Depression: A Network-based Study

Background: Major Depressive Disorder (MDD) is a prevalent mental health condition that affects a significant portion of the general population. Despite the availability of pharmacological, psychological, and novel neurological approaches, optimal outcomes are only achieved in roughly 50% of patients. Traditional Persian Medicine (TPM) recommends a holistic approach to cure diseases based on the etiologic cause of malfunctions. This study aimed to explore the potential of integrating TPM and systems pharmacology approaches for the management of MDD. background: Depression is considered one of the most prevalent mental illnesses worldwide. Depression and the effectiveness of antidepressants are both influenced by genetic factors. Various anti-depressant remedies have been listed in Traditional Persian medicine (TPM) manuscripts, which may be useful as adjuvant therapy in the treatment of depression. The use of network-based studies is becoming more and more crucial for comprehending how medications apply their pharmacological actions. The growing fields of systems pharmacology (SP) and bioinformatics make use of computing to comprehend how drugs function at cellular and molecular levels. A common biological route that involves protein-protein (drug-target) interactions can be mechanistically understood using SP. Methods: A network-based investigation was conducted to explore the molecular mechanisms of TPM-based herbal remedies for depression. The study utilized a network pharmacology approach to identify active compounds, targets, and pathways involved in the treatment of depression. The study also conducted a literature review to identify the effectiveness of TPMbased remedies in treating depression. objective: The objective of the current study was to investigate the molecular mechanisms underlying TPM approach in the treatment of depression. Results: The study identified several active compounds, targets, and pathways involved in the treatment of depression by TPM-based herbal remedies. The study also identified several TPMbased remedies with anti-inflammatory properties that may be effective in treating depression. The literature review supported the potential of TPM remedies for the management of MDD. Conclusion: The integration of TPM and systems pharmacology may provide a holistic insight into the management of MDD. The study's findings suggest the potential of TPM-based remedies with anti-depressant properties, and further research is needed to understand the molecular mechanisms underlying their effectiveness. Integrating TPM-based remedies with bioinformatics may provide a complementary therapeutic avenue to MDD management and improve the quality of life for MDD patients. result: According to our study, many phytochemicals found in the plants used to make TPM-based prescriptions may impact various genes linked to depressive disorders. conclusion: The findings of these investigations suggested that many of the identified herbal remedies may have the potential to treat depression by activating a number of different pathways. The current study encourages further study of TPM as a model for creating efficient complementary therapies.

Wogonin inhibits oxidative stress and vascular calcification via modulation of heme oxygenase-1

Vascular calcification (VC) is highly prevalent and increases the morbidity and mortality of cardiovascular diseases. However, the underlying mechanism remains unclear and there is no effective treatment so far. Interestingly, using systems pharmacology approach, we have predicted that Wogonin (Wog) exhibited potential activity against VC. Then we validated the effect of Wog on VC using human and rat vascular smooth muscle cells (VSMCs), rat arterial rings and vitamin D3-overloaded mouse models. Our results showed that Wog dose-dependently inhibited calcification of VSMCs and rat arterial rings. Consistently, alizarin red staining and calcium content assay confirmed that Wog inhibited aortic calcification in vitamin D3-overloaded mice. Moreover, by constructing the protein regulating network of Wog in suppressing VC, we found heme oxygenase-1 (HMOX-1) was regulated by Wog. Additionally, pathway enrichment analysis revealed that inhibition of reactive oxygen species (ROS) pathway participated in the inhibitory role of Wog in VC and HMOX-1 was also involved in this process. Notably, our study revealed that Wog treatment promoted HMOX-1 expression, and reduced ROS levels in VSMCs. Interestingly, both inhibition of HMOX-1 by ZnPP9 and knockdown of HMOX-1 by siRNA independently eliminated the inhibitory effect of Wog on VC. Finally, administration of Wog suppressed aortic calcification in vitamin D3-overloaded mice and this effect was counteracted by ZnPP9,suggesting the crucial role of HMOX-1 in the inhibitory effect of Wog on VC. Collectively, this study combines systems pharmacology-based strategy and experiments to identify the therapeutic potential of Wog for VC via upregulating HMOX-1 and reducing oxidative stress.

A spatially specified systems pharmacology therapy for axonal recovery after injury.

There are no known drugs or drug combinations that promote substantial central nervous system axonal regeneration after injury. We used systems pharmacology approaches to model pathways underlying axonal growth and identify a four-drug combination that regulates multiple subcellular processes in the cell body and axons using the optic nerve crush model in rats. We intravitreally injected agonists HU-210 (cannabinoid receptor-1) and IL-6 (interleukin 6 receptor) to stimulate retinal ganglion cells for axonal growth. We applied, in gel foam at the site of nerve injury, Taxol to stabilize growing microtubules, and activated protein C to clear the debris field since computational models predicted that this drug combination regulating two subcellular processes at the growth cone produces synergistic growth. Physiologically, drug treatment restored or preserved pattern electroretinograms and some of the animals had detectable visual evoked potentials in the brain and behavioral optokinetic responses. Morphology experiments show that the four-drug combination protects axons or promotes axonal regrowth to the optic chiasm and beyond. We conclude that spatially targeted drug treatment is therapeutically relevant and can restore limited functional recovery.

Cheminformatics and systems pharmacology approaches to unveil the potential plant bioactives to combat COVID-19.

COVID-19 was a global pandemic in the year 2020. Several treatment options failed to cure the disease. Thus, plant-based medicines are becoming a trend nowadays due to their less side effects. Bioactive chemicals from natural sources have been utilised for centuries as treatment options for a variety of ailments. To find out the potent bioactive compounds to counteract COVID-19, we use systems pharmacology and cheminformatics. They use the definitive data and predict the possible outcomes. In this study, we collected a total of 72 phytocompounds from the medicinally important plants such as Garcinia mangostana and Cinnamomum verum, of which 13 potential phytocompounds were identified to be active against the COVID-19 infection based on Swiss Target Prediction and compound target network analysis. These phytocompounds were annotated to identify the specific human receptor that targets COVID-19-specific genes such as MAPK8, MAPK14, ACE, CYP3A4, TLR4 and TYK2. Among these, compounds such as smeathxanthone A, demethylcalabaxanthone, mangostanol, trapezifolixanthone from Garcinia mangostana and camphene from C. verum were putatively target various COVID-19-related genes. Molecular docking results showed that smeathxanthone A and demethylcalabaxanthone exhibit increased binding efficiency towards the COVID-19-related receptor proteins. These compounds also showed efficient putative pharmacoactive properties than the commercial drugs ((R)-remdesivir, favipiravir and hydroxychloroquine) used to cure COVID-19. In conclusion, our study highlights the use of cheminformatics approach to unravel the potent and novel phytocompounds against COVID-19. These phytocompounds may be safer to use, more efficient and less harmful. This study highlights the value of natural products in the search for new drugs and identifies candidates with great promise.

Quantitative Systems Pharmacology & Machine Learning - A match made in heaven or hell?

As pharmaceutical development moves from early stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realising the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modelling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data is highlighted in informing the initial use of ML or mechanistic Quantitative Systems Pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies, to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. Significance Statement We consider when best to apply Machine Learning (ML) and mechanistic Quantitative Systems Pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Integrating animal experiments, mass spectrometry and network-based approach to reveal the sleep-improving effects of Ziziphi Spinosae Semen and γ-aminobutyric acid mixture

BackgroundZiziphi Spinosae Semen (ZSS) is a plant widely used as medicine and food in Asian countries due to its numerous health benefits. γ-aminobutyric acid (GABA), a non-proteinaceous amino acid, is one of the major inhibitory neurotransmitters with a relaxant function. In this study, a system pharmacology approach was employed to assess the effects of a mixture composed of ZSS and GABA (ZSSG) on sleep improvement.MethodsMice were divided into five groups (n = 10) and received either no treatment, sodium pentobarbital, or sodium barbital with diazepam or ZSSG. The effects of ZSSG on sleep quality were evaluated in mice, and differential metabolites associated with sleep were identified among the control, ZSS, GABA, and ZSSG groups. Additionally, network-based ingredient-insomnia proximity analysis was applied to explore the major ingredients.ResultsZSSG significantly improved sleep quality by decreasing sleep latency and prolonging sleep duration in sodium pentobarbital-induced sleeping mouse model (P < 0.05). ZSSG significantly enhanced the brain content of GABA in mice. Furthermore, ZSSG also significantly decreased sleep latency-induced by sodium barbital in mice (P < 0.05). Metabolic analysis revealed significant differences in 10 metabolites between ZSSG group and the groups administering ZSS or GABA. Lastly, using the network-based ingredient screening model, we discovered potential four active ingredients and three pairwise ingredient combinations with synergistic effect on insomnia from ZSSG among 85 ingredients identified by UPLC-Q/TOF–MS. Also, we have constructed an online computation platform.ConclusionOur data demonstrated that ZSSG improved the sleeping quality of mice and helped to balance metabolic disorders-associated with sleep disorders. Moreover, based on the network-based prediction method, the four potential active ingredients in ZSSG could serve as quality markers-associated with insomnia. The network-based framework may open up a new avenue for the discovery of active ingredients of herbal medicine for treating complex chronic diseases or symptoms, such as insomnia.

Systems pharmacology dissection of pharmacological mechanisms of Xiaochaihu decoction against human coronavirus

BackgroundAlthough coronavirus disease 2019 (COVID-19) pandemic is still rage worldwide, there are still very limited treatments for human coronaviruses (HCoVs) infections. Xiaochahu decoction (XCHD), which is one of the traditional Chinese medicine (TCM) prescriptions in Qingfeipaidu decoction (QFPDD), is widely used for COVID-19 treatment in China and able to relieve the symptoms of fever, fatigue, anorexia, and sore throat. To explore the role and mechanisms of XCHD against HCoVs, we presented an integrated systems pharmacology framework in this study.MethodsWe constructed a global herb-compound-target (H-C-T) network of XCHD against HCoVs. Multi-level systems pharmacology analyses were conducted to highlight the key XCHD-regulated proteins, and reveal multiple HCoVs relevant biological functions affected by XCHD. We further utilized network-based prediction, drug-likeness analysis, combining with literature investigations to uncover the key ani-HCoV constituents in XCHD, whose effects on anit-HCoV-229E virus were validated using cytopathic effect (CPE) assay. Finally, we proposed potential molecular mechanisms of these compounds against HCoVs via subnetwork analysis.ResultsBased on the systems pharmacology framework, we identified 161 XCHD-derived compounds interacting with 37 HCoV-associated proteins. An integrated pathway analysis revealed that the mechanism of XCHD against HCoVs is related to TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signaling pathway. Five compounds from XCHD, including betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 exerted inhibitory activity against HCoV-229E virus in Huh7 cells using in vitro CPE assay.ConclusionOur work presented a comprehensive systems pharmacology approach to identify the effective molecules and explore the molecular mechanism of XCHD against HCoVs.

Systems pharmacology-based mechanism exploration of Acanthopanax senticosusin for Alzheimer's disease using UPLC-Q-TOF-MS, network analysis, and experimental validation

BackgroundAlzheimer's disease (AD) is a neurodegenerative disease, characterized by progressive cognitive dysfunction and memory loss. However, the disease-modifying treatments for AD are still lacking. Traditional Chinese herbs, have shown their potentials as novel treatments for complex diseases, such as AD. PurposeThis study was aimed at investigating the mechanism of action (MOA) of Acanthopanax senticosusin (AS) for treatment of AD. MethodsIn this study, we firstly identified the chemical constituents in Acanthopanax senticosusin (AS) utilizing ultra-high performance liquid chromatography coupled with Q-TOF-mass spectrometry (UPLC-Q-TOF-MS), and next built the drug-target network of these compounds. We also performed the systems pharmacology-based analysis to preliminary explore the MOA of AS against AD. Moreover, we applied the network proximity approach to identify the potential anti-AD components in AS. Finally, experimental validations, including animal behavior test, ELISA and TUNEL staining, were conducted to verify our systems pharmacology-based analysis. Results60 chemical constituents in AS were identified via the UPLC-Q-TOF-MS approach. The systems pharmacology-based analysis indicated that AS might exert its therapeutic effects on AD via acetylcholinesterase and apoptosis signaling pathway. To explore the material basis of AS against AD, we further identified 15 potential anti-AD components in AS. Consistently, in vivo experiments demonstrated that AS could protect cholinergic nervous system damage and decrease neuronal apoptosis caused by scopolamine. ConclusionOverall, this study applied systems pharmacology approach, UPLC-Q-TOF-MS, network analysis, and experimental validation to decipher the potential molecular mechanism of AS against AD.

Systems Pharmacology-Based Research on the Mechanism of Danzhi Xiaoyao San in the Treatment of Depression

Depression is a common and serious mental disorder characterized by persistent feeling of sadness and loss of interest in activities. Despite conventional treatments can improve depression symptoms and prevent a recurrence, complementary and alternative therapies are necessary to be involved because of the detrimental effects of the current therapy. Traditional Chinese Medicine (TCM) formulas are considered to be a potential complementary method with special advantages for treating the multiple pathogenesis of depression in a holistic way. However, the underlying pharmacological mechanism of TCM formula on depression therapy remained elusive. Therefore, in current study, a systems pharmacology approach integrating Absorption, Distribution, Metabolism, and Excretion (ADME) filtering, target fishing, gene enrichment analysis, network pharmacology, and pathway analysis was developed to comprehensively explore the multiple therapeutic mechanisms of Danzhi Xiaoyao San (DZXYS) for the treatment of depression. Finally, 111 active components and 112 potential targets of DZXYS were identified through ADME filtering and target fishing. Gene enrichment analysis found that DZXYS played a significant role in treating depression through the regulation of multiple pathogenesis including neurotransmitter systems imbalance, brain derived neurotrophic factor, hypothalamic-pituitary-adrenal axis dysregulation, inflammation, and immune. Moreover, the compound-target and target-pathway networks were constructed to elucidate the therapeutic mechanism of DZXYS from the system perspective. This study proposes a promising way for facilitating in-depth understanding of the complicated therapeutic mechanism of TCM on depression.

Systems pharmacology approach to explore the mechanisms of shufeng Jiedu Capsule on treating H1N1 infection

Background Influenza caused by the H1N1 virus still affects human health. There is currently no effective strategy against H1N1 virus infection. The present study is to evaluate the mechanism of Shufeng Jiedu Capsule (SFJDC) in the treatment of H1N1 infection using an integrated systems pharmacology approach and experimental validation. SFJDC is recommended for the treatment of H1N1 infection in traditional Chinese medicine (TCM), whose mechanism of action is not precise. Methods We systematically analyzed SFJDC using a systematic pharmacology and ADME screening model, and predicted effective targets using systematic drug targeting (SysDT) algorithm. Subsequently, the network of interactions between compounds and targets was built to help in the discovery of new drugs. In addition, the pathway of molecular action was determined by using enrichment analysis from the predicted targets. what is more, molecular docking also applied to predict the specific binding sites and binding capacity of active compounds and related targets, which validated the results of the compounds-targets network (C-T network). Finally, the mechanism of SFJDC effect on autophagy and virus replication in H1N1 virus-infected RAW264.7 mouse macrophage cells was experimentally verified. Results The systematic pharmacology results suggested that 68 candidate compounds were obtained from SFJDC, which interacted with 74 different targets related to inflammation and the immune system. The CCK-8 results showed that different concentrations of SFJDC serum had no significant inhibitory effect on the viability of RAW264.7 cells. LC3-II was significantly increased after virus infection compared to the control group, while it was inhibited by different concentrations of SFJDC serum. H1N1 virus nucleocapsid protein (NP protein) was significantly reduced in the high concentration group, Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α), and viral M1 gene were significantly reduced compared to the H1N1 group. Conclusions The integrated systemic pharmacological approach and experimental validation not only provide a precise explanation of the molecular mechanism of SFJDC in the treatment of H1N1 infection but also provide valuable clues for the development of novel drug strategies to control the H1N1 infection.

Withaferin A for COVID-19: a Network Pharmacology Approach.

COVID-19 has become a global challenge as there are very few treatment options available. This has proved to impact several physiological implications like immunological injury, myocardial infarction, micro-thrombus formation, neurological complications and multi-organ dysfunction. A combination therapy or a systems pharmacology approach can be adopted to fight against COVID-19. Here, we have proposed withaferin A as a system pharmacophore employing molecular docking strategy using AutoDock Vina and utilising different bioinformatics tools like PharmMapper, STRING database and PANTHER Pathway enrichment analysis. Docking results show that withaferin A exhibits a significant binding affinity with P2Y12 receptor, vitamin D-binding protein and annexin A5, hence implying that it could play a role in anti-thrombosis. Protein-protein interaction network showed its importance in innate immune system. Results also show that this molecule may have significant potential to modulate T cell activation too. Text mining results showed association of STAT3 with withaferin A. Our studies propose that withaferin A might also conquer the cytokine storm via STAT3. This study concludes that two strong targets of withaferin A, i.e. vitamin D-binding protein and STAT3, have been identified and that withaferin A can be used as a system pharmacophore for drug development in order to combat COVID-associated complicacies.

Stress resilience-enhancing drugs preserve tissue structure and function in degenerating retina via phosphodiesterase inhibition

Chronic, progressive retinal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy, and retinitis pigmentosa, arise from genetic and environmental perturbations of cellular and tissue homeostasis. These disruptions accumulate with repeated exposures to stress over time, leading to progressive visual impairment and, in many cases, legal blindness. Despite decades of research, therapeutic options for the millions of patients suffering from these disorders remain severely limited, especially for treating earlier stages of pathogenesis when the opportunity to preserve the retinal structure and visual function is greatest. To address this urgent, unmet medical need, we employed a systems pharmacology platform for therapeutic development. Through integrative single-cell transcriptomics, proteomics, and phosphoproteomics, we identified universal molecular mechanisms across distinct models of age-related and inherited retinal degenerations, characterized by impaired physiological resilience to stress. Here, we report that selective, targeted pharmacological inhibition of cyclic nucleotide phosphodiesterases (PDEs), which serve as critical regulatory nodes that modulate intracellular second messenger signaling pathways, stabilized the transcriptome, proteome, and phosphoproteome through downstream activation of protective mechanisms coupled with synergistic inhibition of degenerative processes. This therapeutic intervention enhanced resilience to acute and chronic forms of stress in the degenerating retina, thus preserving tissue structure and function across various models of age-related and inherited retinal disease. Taken together, these findings exemplify a systems pharmacology approach to drug discovery and development, revealing a new class of therapeutics with potential clinical utility in the treatment or prevention of the most common causes of blindness.

Abstract 4876: A systems pharmacology approach to discover synergistic targeted therapy combinations

Abstract The discovery of oncogenic signal transduction in cancer has enabled the development and clinical use of dozens of kinase-directed targeted therapies, but patient response is often transient and resistance invariably develops. In the setting of BRAF-mutant melanoma, for instance, a majority of patients relapse within one year of initiating targeted therapy. Combination therapies blocking compensatory signaling pathways provide a strategy for boosting treatment efficacy and delaying resistance. To find synergistic kinase inhibitor combinations, we used a multimodal systems pharmacology approach to quantify the early adaptive signaling and gene regulatory dynamics in patient-derived BRAFV600E-mutant melanoma cells under oncogene inhibition. Cells were treated with vemurafenib or vehicle and sampled longitudinally at timepoints spanning minutes to days. To measure signaling dynamics, thousands of protein phosphorylation sites covering commonly altered signaling axes in cancer were motif-enriched by immunoprecipitation, barcoded with isobaric tandem mass tags (TMT), and quantified by high-resolution mass spectrometry (MS). The resulting time-resolved phosphoproteomics data showed rapid (&amp;lt;15 minutes) and potent (&amp;gt;8-fold) downregulation of ERK1/2, confirming strong BRAF inhibition. Statistical integration of signaling measurements with time-series RNA-seq data collected at the same timepoints enabled reconstruction of multi-scale regulatory networks governing the adaptive response to oncogene inhibition. In particular, we observed early induction of SRC-family kinase (SFK) signaling and a broad cytoskeletal signaling module, implicating a compensatory prosurvival signaling program. The induction intensified over the 3-day treatment window and was fully reversed following a 6-day drug holiday, suggesting a reversible nature of drug adaptation. Immunoblotting confirmed sustained loss of ERK1/2 activity and concomitant elevated SFK phosphorylation following drug treatment. Accumulation of reactive oxygen species, a known activator of SFKs, strongly correlated with SFK activity, and both were ablated following treatment with the antioxidant precursor N-acetylcysteine. This adaptive response predicted a significant degree of synergy between vemurafenib and the pan-SFK inhibitor dasatinib, which was validated in a panel of patient-derived melanoma cell lines and in melanoma xenograft mouse models. Targeted MS of phospho-ERK1/2 and canonical SFK substrates, including CTTN, PXN, and PAG1, validated the mechanism of action of this combination. Complementary data from patient-derived non small-cell lung cancer (NSCLC) cells under tyrosine kinase inhibitor treatment demonstrates the generality of our integrative approach, and supports the notion that SFK activation may be a hallmark response to oncogenic RTK-RAS-ERK pathway inhibition. Citation Format: Cameron T. Flower, Chunmei Liu, James R. Heath, Wei Wei, Forest M. White. A systems pharmacology approach to discover synergistic targeted therapy combinations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4876.

Abstract 1119: Systems pharmacology approaches to study tumor drug mechanism of action

Abstract Precision cancer medicine is predicated on the ability to identify drugs whose mechanism of action (MoA) matches one or more key tumor dependencies. Although the structure of many target proteins is available and their binding affinity to specific compounds can be experimentally assessed, drug efficacy and toxicity are infrequently determined based on single target affinity. Rather, the pharmacologic property of drugs is often the result of its complex polypharmacology, as mediated by both unknown or poorly characterized off-target effects (i.e., lower-affinity binding proteins) as well as by tissue-specific secondary effector proteins (i.e., non-binding) that are undetectable by traditional assays. MoA elucidation typically relies on in vitro affinity binding assays tailored to specific protein classes, such as kinases or metabolic enzymes, which cannot reveal critical high- and low-affinity binding targets among other protein classes, as well as context-specific downstream effectors. The goal of this study is to address critical unresolved questions in cancer pharmacology by characterizing the proteome-wide MoA of oncology drugs as a critical, yet highly elusive step necessary to fully understand their clinical efficacy and toxicity. To elucidate proteome-wide, drug-mediated changes in protein activity (drug MoA), we performed network-based analyses of genome-wide RNA-seq profiles representing the response of patient-matched cell lines to a comprehensive repertoire of clinically relevant drugs. More specifically, we generated genome-wide drug perturbation profiles from 23 cancer cell representing high-fidelity models of patients in clinical cohorts representing distinct tumor subtypes, using &amp;gt; 700 oncology drugs. This represents the largest resources of functionally annotated, clinically-relevant, genome-wide perturbational profiles for clinically relevant drugs, named PanACEA. VIPER-based analysis of this resource effectively elucidated the effect of each individual drug on the activity of ~6,500 regulatory and signaling proteins, in each of the 23 distinct represented cancer subtypes. Analyses of these data, using a graph theory approach, helped elucidate critical functional relationship between individual drugs and group drugs into functionally-distinct modules within each cancer context, thus providing critical insight into both drug MoA and polypharmacology. These analyses also effective drug inhibitors for critical cancer dependencies represented by transcription factors and co-factors, considered undruggable, that were experimentally validated. Finally, integrative analysis of PANCEA predictions and Cancer Dependency Map data significantly improved our understanding of cancer-related drug pharmacology, thus providing a unique resource for cancer related studies, which has been used in key clinical and pre-clinical trials, both published and in review. Citation Format: Lucas Zhongming Hu, Eugene Douglass, Ron Realubit, Karan Charles, Mariano Alvarez, Andrea Califano. Systems pharmacology approaches to study tumor drug mechanism of action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1119.

Systems Pharmacology and Network Analysis to Advance Pharmacogenomics and Precision Medicine Decisions in Type-2 Diabetes Therapy

Diabetes mellitus type-2 (DMT2) molecular pathophysiology is still challenging since the disease represents a complex, multifactorial metabolic disease caused by polygenic defects and environmental factors. In addition, the resulting secondary organ complications can be affected by various environmental and life-style factors over the years. The metabolic imbalance in DMT2 is manifested by the dysfunction of pancreatic β-cells in secreting insulin and the inability of other tissue cells to respond to insulin and utilize blood glucose. However, over recent years, through the advances in genomics and molecular analysis, several genes and microRNAs have been shown to be correlated as potential biomarkers with DMT2 prognosis, diagnosis, and therapy. Furthermore, drug therapy and clinical pharmacology have benefited from pharmacogenomics in a manner where the molecular knowledge can be translated into clinical information aiming to improve precision and personalized medicine therapeutic methodologies in healthcare. In this work, using systems pharmacology and network analysis approaches, we comprehensively assessed the molecular and genomics data associated with DMT2 to: (a) Better understand miRNA, gene, and drug associations; (b) Create connectivity and interaction maps of practical clinical utility; and (c) Facilitate the application of precision medicine therapeutic decisions in group and individual patients. Moreover, in order for the clinical pharmacology guidelines to be implemented in parallel with the generated molecular data, we also carried out an assessment of drug interactions in specific pharmacological classes that affect DMT2 pharmacotherapy outcomes. Overall, the proposed methodology and the results obtained: (a) Enrich our understanding of DMT2 molecular pathophysiology; (b) Unveil important biomarker and drug-gene pharmacogenomics associations; (c) Help the use of personalized therapy options; and (d) Allow precision medicine concepts to be broadly exploited in new therapeutic developments and within the clinical setting.

Analysis of KRAS–Ligand Interaction Modes and Flexibilities Reveals the Binding Characteristics

KRAS, a common human oncogene, has been recognized as a critical drug target in treating multiple cancers. After four decades of effort, one allosteric KRAS drug (Sotorasib) has been approved, inspiring more KRAS-targeted drug research. Here, we provide the features of KRAS binding pockets and ligand-binding characteristics of KRAS complexes using a structural systems pharmacology approach. Three distinct binding sites (conserved nucleotide-binding site, shallow Switch-I/II pocket, and allosteric Switch-II/α3 pocket) are characterized. Ligand-binding features are determined based on encoded KRAS-inhibitor interaction fingerprints. Finally, the flexibility of the three distinct binding sites to accommodate different potential ligands, based on MD simulation, is discussed. Collectively, these findings are intended to facilitate rational KRAS drug design.

A combined physiologically-based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer's disease.

Antibody-mediated removal of aggregated β-amyloid (Aβ) is the current, most clinically advanced potential disease-modifying treatment approach for Alzheimer's disease. We describe a quantitative systems pharmacology (QSP) approach of the dynamics of Aβ monomers, oligomers, protofibrils, and plaque using a detailed microscopic model of Aβ40 and Aβ42 aggregation and clearance of aggregated Aβ by activated microglia cells, which is enhanced by the interaction of antibody-bound Aβ. The model allows for the prediction of Aβ positron emission tomography (PET) imaging load as measured by a standardized uptake value ratio. A physiology-based pharmacokinetic model is seamlessly integrated to describe target exposure of monoclonal antibodies and simulate dynamics of cerebrospinal fluid (CSF) and plasma biomarkers, including CSF Aβ42 and plasma Aβ42 /Aβ40 ratio biomarkers. Apolipoprotein E genotype is implemented as a difference in microglia clearance. By incorporating antibody-bound, plaque-mediated macrophage activation in the perivascular compartment, the model also predicts the incidence of amyloid-related imaging abnormalities with edema (ARIA-E). The QSP platform is calibrated with pharmacological and clinical information on aducanumab, bapineuzumab, crenezumab, gantenerumab, lecanemab, and solanezumab, predicting adequately the change in PET imaging measured amyloid load and the changes in the plasma Aβ42 /Aβ40 ratio while slightly overestimating the change in CSF Aβ42 . ARIA-E is well predicted for all antibodies except bapineuzumab. This QSP model could support the clinical trial design of different amyloid-modulating interventions, define optimal titration and maintenance schedules, and provide a first step to understand the variability of biomarker response in clinical practice.