Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

The quantitative systems pharmacology (QSP) approach is widely applied to address various essential questions in drug discovery and development, such as identification of the mechanism of action of a therapeutic agent, patient stratification, and the mechanistic understanding of the progression of disease. In this review article, we show the current landscape of the application of QSP modeling using a survey of QSP publications over 10 years from 2013 to 2022. We also present a use case for the risk assessment of hyperkalemia in patients with diabetic nephropathy treated with mineralocorticoid receptor antagonists (MRAs, renin-angiotensin-aldosterone system inhibitors), as a prospective simulation of late clinical development. A QSP model for generating virtual patients with diabetic nephropathy was used to quantitatively assess that the nonsteroidal MRAs, finerenone and apararenone, have a lower risk of hyperkalemia than the steroidal MRA, eplerenone. Prospective simulation studies using a QSP model are useful to prioritize pharmaceutical candidates in clinical development and validate mechanism-based pharmacological concepts related to the risk-benefit, before conducting large-scale clinical trials.

Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.

BACKGROUND: How to screen and identify the effective components in the complex substance system is one of the core issues in achieving the modernization of traditional Chinese medicine (TCM) formulas. However, it is still challenging to systematically screen out the effective components from the hundreds or thousands of components in a TCM formula.
OBJECTIVE: An innovative five-layer-funnel filtering mode stepwise integrating chemical profile, quantitative analysis, xenobiotic profile, network pharmacology and bioactivity evaluation was successfully presented to discover the effective components and implemented on a case study of Zhishi-Xiebai-Guizhi decoction (ZXG), a well-known TCM formula for coronary heart disease (CHD).
METHODS: Initially, the chemical profile of ZXG was systemically characterized. Subsequently, the representative constituents were quantitatively analyzed. In the third step, the multi-component xenobiotics profile of ZXG was systemically delineated, and the prototypes absorbed into the blood were identified and designated as the primary bioavailable components. Next, an integrated network of \"bioavailable components-CHD targets-pathways-therapeutic effects\" was constructed, and the crucial bioavailable components of ZXG against CHD were screened out. Lastly, the bioactivities of crucial bioavailable components were further evaluated to pinpoint effective components.
RESULTS: First of all, the chemical profile of ZXG was systemically characterized with the detection of 201 components. Secondly, 37 representative components were quantified to comprehensively describe its content distribution characteristics. Thirdly, among the quantified components, 24 bioavailable components of ZXG were identified based on the multi-component xenobiotic profile. Fourthly, an integrated network led to the identification of 11 crucial bioavailable components against CHD. Ultimately, 9 components (honokiol, magnolol, naringenin, magnoflorine, hesperidin, hesperetin, naringin, neohesperidin and narirutin) exhibiting myocardial protection in vitro were identified as effective components of ZXG for the first time.
CONCLUSIONS: Overall, this innovative strategy successfully identified the effective components of ZXG for the first time. It could not only significantly contribute to elucidating the therapeutic mechanism of ZXG in the treatment of CHD, but also serve as a helpful reference for the systematic discovery of effective components as well as ideal quality markers in the quality assessment of TCM formulas.

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.

Receptor occupancy assays applied in clinical studies provide insights into pharmacokinetic-pharmacodynamic relationships for therapeutic antibodies. When measured by different assays, however, receptor occupancy results can be controversial, as was observed for nivolumab, a monoclonal antibody targeting programmed cell death 1 (PD-1) receptor. We suggested an explanation of results obtained and a mechanistic approach based on specific features of the receptor occupancy assays: measurement of the free or bound receptor, normalized to the baseline or at each time point. The approach was evaluated against controversial clinical data on PD-1 receptor occupancy by nivolumab. It was shown that receptor occupancy measured by different assays might vary substantially if the internalization rate of the bound receptor is higher than the rate of degradation of the free receptor. Equations proposed in this work can be applied in quantitative systems pharmacology models to describe target receptor occupancy by different therapeutic antibodies.

BACKGROUND: Atherosclerosis (AS) is a chronic disease that is associated with high morbidity. However, therapeutic approaches are limited. Wu-Zhu-Yu decoction (WZYD) is a well-known traditional Chinese medicine prescription that is traditionally used to treat headaches and vomiting. Modern studies have demonstrated the cardiotonic effects of WZYD. However, whether WZYD can alleviate AS and its underlying mechanisms remain unclear.
OBJECTIVE: This study aims to investigate the antiatherosclerotic efficacy of WZYD and illustrate its potential mechanisms using an integrated approach combining in vivo and in vitro assessments, including metabolomics, network pharmacology, cell experiments, and molecular docking analyses.
METHODS: In this work, an atherosclerotic mouse model was established by administering a high-fat diet to apolipoprotein-E deficient (ApoE-/-) mice for twelve weeks. Meanwhile, the mice were intragastrically administered WZYD at different dosages. Efficacy evaluation was performed through biochemical and histopathological assessments. The potential active constituents, metabolites, and targets of WZYD in atherosclerosis were predicted by metabolomics combined with network pharmacology analysis, the constituents and targets were further assessed through cell experiments and molecular docking analysis.
RESULTS: WZYD decreased the lipid levels in serum, reduced the areas of aortic lesions, and attenuated intimal thickening, which had antiatherosclerotic effects in ApoE-/- mice. Metabolomics and network pharmacology approach revealed that the ten constituents (6-shogaol, evodiamine, isorhamnetin, quercetin, beta-carotene, 8-gingerol, kaempferol, 6-paradol, 10-gingerol, and 6-gingerol) of WZYD affected 24 metabolites by acting on the candidate targets, thus resulting in changes in five metabolic pathways (sphingolipid metabolism; glycine, serine and threonine metabolism; arachidonic acid metabolism; tryptophan metabolism; and fatty acid biosynthesis pathway). Cell experiments indicated that the ten key compounds showed antiproliferative effects on the vascular smooth muscle cell. Moreover, the key compounds exhibited direct interactions with the key targets, as assessed by molecular docking analysis.
CONCLUSIONS: This study revealed that WZYD exerted therapeutic effects on atherosclerosis, and the potential mechanisms were elucidated. Furthermore, it offered a powerful integrated strategy for studying the efficacy of traditional Chinese medicine and exploring its active components and possible mechanisms.

BACKGROUND: The current network pharmacology model focuses mainly on static and qualitative characterisation between drugs and targets or molecular pathway networks, but it does not reflect the multi-scale, dynamic and quantitative process of drug action.
OBJECTIVE: In this study, we developed a new model known as quantitative and network pharmacology (QNP) to characterise the dynamic and quantitative interventions of drugs within a multi-scale biological network.
METHODS: Firstly, we used a systems biology method to construct a molecule-cell dynamic network model to simulate the pathological processes of diseases. Secondly, according to the principles of enzymatic kinetics, we generated a multi-scale drug intervention model to simulate the intervention of drugs in multi-scale networks at different concentrations and pathological stages. Finally, we took rhein treatment of renal interstitial fibrosis (RIF) as an example to illustrate the QNP model.
RESULTS: We successfully constructed the a QNP model that includes both a multi-scale dynamic network disease model and drug intervention model. The QNP model accurately simulated the pathological process of RIF, and the simulation results were validated by a series of cell and animal experiments. Meanwhile, the QNP model demonstrated that rhein can delay the pathological process at the studied concentrations of 5 nM, 10 nM, and 20 nM, and can also exert a better therapeutic effect on fibrosis before the proliferation stage of RIF. Furthermore, through uncertainty and sensitivity analysis, we identified that FAK and Smad3 may be potential targets for RIF.
CONCLUSIONS: Our QNP model provides a molecular-cellular understanding of the pathological mechanisms of RIF, serving as a new approach and strategy for the construction of dynamic multi-scale network model of diseases and drug intervention.

With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.

Personal genomic analysis was used for molecular diagnosis and pharmacogenomics in a 53-year-old female suffering from alternating depressive and dysphoric episodes. A total of 52 genes and 108 SNPs were analyzed in the whole genome. Results from the pharmacogenomic analysis were consistent with the pharmacological history and indicate mutations associated with low monoaminergic tone, but also a hyperactive 5HT2A receptor, a feature that associates to a high probability of developing a bipolar condition, especially under 5-hydroxytryptamine potentiating pharmacology. This aligns with the patient developing dysphoria with high clomipramine. The pharmacokinetic genomics pointed out to some absorption, distribution, metabolism, and excretion (ADME) alterations that can lower or nullify drug\'s activity. A personalized regimen was proposed, with a positive outcome after 1 year.