Systems pharmacology-based drug discovery from Amaryllidaceae alkaloids and investigation of mechanisms of action in treatment of Alzheimer's disease.

OBJECTIVES: Given the success of galanthamine in treating Alzheimer's disease, this study aims to establish an effective method to find drugs from Amaryllidaceae alkaloids and to clarify its mechanism in treating Alzheimer's disease. METHODS: The pharmacodynamic basis and mechanism of action between Amaryllidaceae alkaloids and Alzheimer's disease were explored by constructing a compound-target-disease network, targets protein-protein interaction, gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and molecular docking verification. KEY FINDINGS: In total, a chemical library of 357 potential alkaloids was constructed. A total of 100 active alkaloid components were identified. Thirty-nine associated targets were yielded based on network construction, and the key targets were defined as HSP90AA1, ESR1, NOS3, PTGS2, and PPARG using protein-protein interaction network. Gene ontology items (490) and 68 Kyoto Encyclopedia of Genes and Genomes pathways were selected through the enrichment of target functions, including neuroactive ligand-receptor interaction, calcium signaling pathway, cAMP signaling pathway, Alzheimer disease, and serotonergic synapse that were related to Alzheimer's disease. Lastly, molecular docking demonstrated good stability in combining selected alkaloids with targets. CONCLUSIONS: This study explained the mechanisms of Amaryllidaceae alkaloids in preventing and treating Alzheimer's disease and established a novel strategy to discover new drugs from biological chemical sources.

[Challenges in Understanding the Biological and Pharmacological Responses Based on Emergent Complexity in Biological Systems: From Bone Metabolism to General Physiology].

Biological systems are complex, and although researchers strive to understand them, the accumulated knowledge often complicates integrative comprehension. Consolidating this knowledge can provide insights into the landscape of specific biological events. Our study on bone metabolism, focusing on the behavior of the receptor activator of nuclear factor kappa B (RANK) and its ligand (RANKL) highlighted the challenges in understanding its role across different cell types. At the same time, the study underscores the importance of exploring interactions between various players (cell types and genes/proteins) in complex systems, which is a core focus of systems biology. Analysis by mathematical models is a potentially powerful tool for describing the dynamic behavior of components in the interaction networks. However, such model-based analyses are limited by parameter availability and reliability. To address this, we proposed two approaches, i.e., sequential simulation and system-wide behavior constraints. Sequential simulation of small dynamic models offers potential in reproducing behavior in larger networks, as seen in toxicity analysis of sunitinib-related adverse effects. System-wide constraints derived from "homeostasis" help reduce the parameter search space in large-scale models, as demonstrated in model-based analysis of the effects of non-steroidal anti-inflammatory drugs (NSAIDs) on the arachidonic acid pathway. These analytical approaches offer insights into biological system dynamics and can enhance our understanding of pharmacological effects that result from perturbations in complexities of biological systems.

Exploring the underlying mechanisms of Danshen-Shanzha Decoction on coronary heart disease: An integrated analysis combining pharmacoinformatics and experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: The Danshen-Shanzha Decoction (DSD) is a renowned herbal combination consisting of the root of Salvia miltiorrhiza Bunge (known as Danshen in Chinese) and the fruits of Crataegus pinnatifida Bunge (known as Shanzha in Chinese), which has exhibited remarkable clinical efficacy in the treatment of coronary heart disease (CHD) in traditional Chinese medicine, with its earliest recorded application dating to around 202 BCE during the Han Dynasty. Despite significant advancements in the fundamental research and clinical applications of DSD over the past few decades, the precise bioactive components as well as the underlying mechanisms responsible for its protective effect on CHD remain unelucidated. AIM OF THE STUDY: The present study was designed to elucidate the bioactive components and potential mechanism of DSD in the treatment of CHD using in silico technologies integrated with pharmacoinformatic methods and experimental validation. MATERIALS AND METHODS: The chemical components of DSD were analyzed and identified using UPLC-Q-TOF-MS. Pharmacoinformatic-based methods were employed to comprehensively investigate the principal active components and targets of DSD for treating CHD. GO and KEGG pathway analyses were utilized to elucidate the underlying mechanism responsible for DSD's efficacy against CHD. Molecular docking and molecular dynamics simulation were performed to assess the binding affinity between active components and putative targets. Furthermore, surface plasmon resonance (SPR) was carried out to verify the affinity and kinetic characteristics of major components to STAT3 protein. Subsequently, a series of in vitro experiments, including cell viability test, flow cytometric analysis, ELISA and western blotting, were conducted to validate the predicted results in an oxygen-glucose deprivation (OGD)-stimulated H9c2 model. RESULTS: A total of 96 compounds were characterized by UPLC-Q-TOF-MS, and 281 overlapping targets were identified through pharmacoinformatic-based methods. Among these, ten critical compounds were determined as the core active components of DSD. The core targets associated with the development of CHD included STAT3, SRC, TP53, JUN, and AKT1. Notably, Dihydrotanshinone I and (+)-Epicatechin exhibited strong binding affinity towards STAT3. The potential mechanisms by which DSD modulates the pathological progression of CHD were predicted to involve inflammation, oxidative stress, and apoptosis. Importantly, the cytoprotective effect of DSD against apoptosis was confirmed in OGD-stimulated H9c2 cells, as evidenced by the upregulation of Bcl-2 expression and downregulation of both Bax and cleaved caspase-3 expressions upon DSD treatment. Furthermore, DSD significantly enhanced the phosphorylated protein expressions of JAK2 and STAT3 compared to the OGD group, suggesting its potential role in modulating related signaling pathways. CONCLUSIONS: The current study successfully fills the gap in the understanding of the chemical profiles of DSD, predicting its active components, potential targets, and molecular mechanisms in the treatment of CHD. These findings not only provide a valuable strategy but also robust data support for future investigations into DSD, thereby facilitating the identification of novel therapeutic targets for traditional Chinese medicines in the battle against CHD.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma.

PURPOSE: This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. RESULTS: In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. CONCLUSION: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.

Decoding the Foodome: Molecular Networks Connecting Diet and Health.

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma.

Purpose: This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.

Real-World Application of a Quantitative Systems Pharmacology (QSP) Model to Predict Potassium Concentrations from Electronic Health Records: A Pilot Case towards Prescribing Monitoring of Spironolactone.

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.

The effects of single and multiple resistance mechanisms on bacterial response to meropenem.

OBJECTIVES: Meropenem is commonly used against Pseudomonas aeruginosa. Traditionally, the time unbound antibiotic concentration exceeds the MIC (fT>MIC) is used to select carbapenem regimens. We aimed to characterize the effects of different baseline resistance mechanisms on bacterial killing and resistance emergence; evaluate whether fT>MIC can predict these effects; and, develop a novel Quantitative and Systems Pharmacology (QSP) model to describe the effects of baseline resistance mechanisms on the time-course of bacterial response. METHODS: Seven isogenic P. aeruginosa strains with a range of resistance mechanisms and MICs were used in 10-day hollow-fiber infection model studies. Meropenem pharmacokinetic profiles were simulated for various regimens (t1/2,meropenem = 1.5 h). All viable counts on drug-free, 3 × MIC, and 5 × MIC meropenem-containing agar across all strains, five regimens, and control (n = 90 profiles) were simultaneously subjected to QSP modeling. Whole genome sequencing was completed for total population samples and emergent resistant colonies at 239 h. RESULTS: Regimens achieving ≥98%fT>1×MIC suppressed resistance emergence of the mexR knockout strain. Even 100%fT>5 × MIC failed to achieve this against the strain with OprD loss and the ampD and mexR double-knockout strain. Baseline resistance mechanisms affected bacterial outcomes, even for strains with the same MIC. Genomic analysis revealed that pre-existing resistant subpopulations drove resistance emergence. During meropenem exposure, mutations in mexR were selected in strains with baseline oprD mutations, and vice versa, confirming these as major mechanisms of resistance emergence. Secondary mutations occurred in lysS or argS, coding for lysyl and arginyl tRNA synthetases, respectively. DISCUSSION: The QSP model well-characterized all bacterial outcomes of the seven strains simultaneously, which fT>MIC could not.

Santalum album L. alleviates cardiac function injury in heart failure by synergistically inhibiting inflammation, oxidative stress and apoptosis through multiple components.

BACKGROUND: Heart failure (HF) is a complex cardiovascular syndrome with high mortality. Santalum album L. (SAL) is a traditional Chinese medicine broadly applied for various diseases treatment including HF. However, the potential active compounds and molecular mechanisms of SAL in HF treatment are not well understood. METHODS: The active compounds and possible mechanisms of action of SAL were analyzed and validated by a systems pharmacology framework and an ISO-induced mouse HF model. RESULTS: We initially confirmed that SAL alleviates heart damage in ISO-induced HF model. A total of 17 potentially active components in SAL were identified, with Luteolin (Lut) and Syringaldehyde (SYD) in SAL been identified as the most effective combination through probabilistic ensemble aggregation (PEA) analysis. These compounds, individually and in their combination (COMB), showed significant therapeutic effects on HF by targeting multiple pathways involved in anti-oxidation, anti-inflammation, and anti-apoptosis. The active ingredients in SAL effectively suppressed inflammatory mediators and pro-apoptotic proteins while enhancing the expression of anti-apoptotic factors and antioxidant markers. Furthermore, the synergistic effects of SAL on YAP and PI3K-AKT signaling pathways were further elucidated. CONCLUSIONS: Mechanistically, the anti-HF effect of SAL is responsible for the synergistic effect of anti-inflammation, antioxidation and anti-apoptosis, delineating a multi-targeted therapeutic strategy for HF.

Screening of potentially active compounds against rheumatoid arthritis in the Juan-Bi decoction using systems pharmacology and animal experiments.

Background: The Juan-Bi decoction (JBD) is a classic traditional Chinese medicines (TCMs) prescription for the treatment of rheumatoid arthritis (RA). However, the active compounds of the JBD in RA treatment remain unclear. Aim: The aim of this study is to screen effective compounds in the JBD for RA treatment using systems pharmacology and experimental approaches. Method: Botanical drugs and compounds in the JBD were acquired from multiple public TCM databases. All compounds were initially screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical properties, and then a target prediction was performed. RA pathological genes were acquired from the DisGeNet database. Potential active compounds were screened by constructing a compound-target-pathogenic gene (C-T-P) network and calculating the cumulative interaction intensity of the compounds on pathogenic genes. The effectiveness of the compounds was verified using lipopolysaccharide (LPS)-induced RAW.264.7 cells and collagen-induced arthritis (CIA) mouse models. Results: We screened 15 potentially active compounds in the JBD for RA treatment. These compounds primarily act on multiple metabolic pathways, immune pathways, and signaling transduction pathways. Furthermore, in vivo and in vitro experiments showed that bornyl acetate (BAC) alleviated joint damage, and inflammatory cells infiltrated and facilitated a smooth cartilage surface via the suppression of the steroid hormone biosynthesis. Conclusion: We screened potential compounds in the JBD for the treatment of RA using systems pharmacology approaches. In particular, BAC had an anti-rheumatic effect, and future studies are required to elucidate the underlying mechanisms.

An update on the novel methods for the discovery of antiseizure and antiepileptogenic medications: where are we in 2024?

INTRODUCTION: Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored. AREAS COVERED: Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective. EXPERT OPINION: In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC.

Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

Activation of GABA type A receptor is involved in the anti-insomnia effect of Huanglian Wendan Decoction.

Huanglian Wendan Decoction (HWD) is a traditional Chinese medicine (TCM) prescribed to patients diagnosed with insomnia, which can achieve excellent therapeutic outcomes. As positively modulating the γ-aminobutyric acid (GABA) type A receptors (GABAARs) is the most effective strategy to manage insomnia, this study aimed to investigate whether the activation of GABAARs is involved in the anti-insomnia effect of HWD. We assessed the metabolites of HWD using LC/MS and the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and tested the pharmacological activity in vitro and in vivo using whole-cell patch clamp and insomnia zebrafish model. In HEK293 cells expressing α1ß3γ2L GABAARs, HWD effectively increased the GABA-induced currents and could induce GABAAR-mediated currents independent of the application of GABA. In the LC-MS (QToF) assay, 31 metabolites were discovered in negative ion modes and 37 metabolites were found in positive ion modes, but neither three selected active metabolites, Danshensu, Coptisine, or Dihydromyricetin, showed potentiating effects on GABA currents. 62 active metabolites of the seven botanical drugs were collected based on the TCMSP database and 19 of them were selected for patch-clamp verification according to the virtual docking simulations and other parameters. At a concentration of 100 µM, GABA-induced currents were increased by (+)-Cuparene (278.80% ± 19.13%), Ethyl glucoside (225.40% ± 21.77%), and ß-Caryophyllene (290.11% ± 17.71%). In addition, (+)-Cuparene, Ethyl glucoside, and ß-Caryophyllene could also serve as positive allosteric modulators (PAMs) and shifted the GABA dose-response curve (DRC) leftward significantly. In the PCPA-induced zebrafish model, Ethyl glucoside showed anti-insomnia effects at concentrations of 100 µM. In this research, we demonstrated that the activation of GABAARs was involved in the anti-insomnia effect of HWD, and Ethyl glucoside might be a key metabolite in treating insomnia.

Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models.

Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.

Prediction of the liver safety profile of a first-in-class myeloperoxidase inhibitor using quantitative systems toxicology modeling.

The novel myeloperoxidase inhibitor verdiperstat was developed as a treatment for neuroinflammatory and neurodegenerative diseases. During development, a computational prediction of verdiperstat liver safety was performed using DILIsym v8A, a quantitative systems toxicology (QST) model of liver safety.A physiologically-based pharmacokinetic (PBPK) model of verdiperstat was constructed in GastroPlus 9.8, and outputs for liver and plasma time courses of verdiperstat were input into DILIsym. In vitro experiments measured the likelihood that verdiperstat would inhibit mitochondrial function, inhibit bile acid transporters, and generate reactive oxygen species (ROS); these results were used as inputs into DILIsym, with two alternate sets of parameters used in order to fully explore the sensitivity of model predictions. Verdiperstat dosing protocols up to 600 mg BID were simulated for up to 48 weeks using a simulated population (SimPops) in DILIsym.Verdiperstat was predicted to be safe, with only very rare, mild liver enzyme increases as a potential possibility in highly sensitive individuals. Subsequent Phase 3 clinical trials found that ALT elevations in the verdiperstat treatment group were generally similar to those in the placebo group. This validates the DILIsym simulation results and demonstrates the power of QST modelling to predict the liver safety profile of novel therapeutics.

Application of quantitative protein mass spectrometric data in the early predictive analysis of membrane-bound target engagement by monoclonal antibodies.

Model-informed drug discovery advocates the use of mathematical modeling and simulation for improved efficacy in drug discovery. In the case of monoclonal antibodies (mAbs) against cell membrane antigens, this requires quantitative insight into the target tissue concentration levels. Protein mass spectrometry data are often available but the values are expressed in relative, rather than in molar concentration units that are easier to incorporate into pharmacokinetic models. Here, we present an empirical correlation that converts the parts per million (ppm) concentrations in the PaxDb database to their molar equivalents that are more suitable for pharmacokinetic modeling. We evaluate the insight afforded to target tissue distribution by analyzing the likely tumor-targeting accuracy of mAbs recognizing either epidermal growth factor receptor or its homolog HER2. Surprisingly, the predicted tissue concentrations of both these targets exceed the Kd values of their respective therapeutic mAbs. Physiologically based pharmacokinetic (PBPK) modeling indicates that in these conditions only about 0.05% of the dosed mAb is likely to reach the solid tumor target cells. The rest of the dose is eliminated in healthy tissues via both nonspecific and target-mediated processes. The presented approach allows evaluation of the interplay between the target expression level in different tissues that determines the overall pharmacokinetic properties of the drug and the fraction that reaches the cells of interest. This methodology can help to evaluate the efficacy and safety properties of novel drugs, especially if the off-target cell degradation has cytotoxic outcomes, as in the case of antibody-drug conjugates.

A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.

Quantitative Proteomics for Translational Pharmacology and Precision Medicine: State of The Art and Future Outlook.

Over the past 20 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. A symposium at the 25th North American ISSX meeting, in Boston, in September 2023, was held to explore current and emerging applications of quantitative proteomics in translational pharmacology and strategies for improved integration into model-informed drug development based on practical experience of each of the presenters. A summary of the talks and discussions is presented in this perspective alongside future outlooks that were outlined for future meetings. Significance Statement This perspective explores current and emerging applications of quantitative proteomics in translational pharmacology and precision medicine, and outlines outlooks for improved integration into model-informed drug development.