Development of bispecific T cell engagers: harnessing quantitative systems pharmacology.

Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.

Safety pharmacology 2023 and implementation of the ICH E14/S7B Q&A guidance document.

This editorial prefaces the annual themed issue on safety pharmacology (SP) methods published since 2004 in the Journal of Pharmacological and Toxicological Methods (JPTM). We highlight here the content derived from the recent 2022 Safety Pharmacology Society (SPS) and Canadian Society of Pharmacology and Therapeutics (CSPT) joint meeting held in Montreal, Quebec, Canada. The meeting also generated 179 abstracts (reproduced in the current volume of JPTM). As in previous years the manuscripts reflect various areas of innovation in SP including a comparison of the sensitivity of cross-over and parallel study designs for QTc assessment, use of human-induced pluripotent stem cell (hi-PSC) neuronal cell preparations for use in neuropharmacological safety screening, and hiPSC derived cardiac myocytes in assessing inotropic adversity. With respect to the latter, we anticipate the emergence of a large data set of positive and negative controls that will test whether the imperative to miniaturize, humanize and create a high throughput process is offset by any loss of precision and accuracy.

Machine learning for synergistic network pharmacology: a comprehensive overview.

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.

Quantitative systems pharmacology of the eye: Tools and data for ocular QSP.

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.

Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.

3D bioprinting of complex tissues in vitro: state-of-the-art and future perspectives.

The pharmacology and toxicology of a broad variety of therapies and chemicals have significantly improved with the aid of the increasing in vitro models of complex human tissues. Offering versatile and precise control over the cell population, extracellular matrix (ECM) deposition, dynamic microenvironment, and sophisticated microarchitecture, which is desired for the in vitro modeling of complex tissues, 3D bio-printing is a rapidly growing technology to be employed in the field. In this review, we will discuss the recent advancement of printing techniques and bio-ink sources, which have been spurred on by the increasing demand for modeling tactics and have facilitated the development of the refined tissue models as well as the modeling strategies, followed by a state-of-the-art update on the specialized work on cancer, heart, muscle and liver. In the end, the toxicological modeling strategies, substantial challenges, and future perspectives for 3D printed tissue models were explored.

Recent applications of quantitative systems pharmacology and machine learning models across diseases.

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

A framework for simplification of quantitative systems pharmacology models in clinical pharmacology.

Quantitative systems pharmacology (QSP) is a relatively new discipline within modelling and simulation that has gained wide attention over the past few years. The application of QSP models spans drug-target identification and validation, through all drug development phases as well as clinical applications. Due to their detailed mechanistic nature, QSP models are capable of extrapolating knowledge to predict outcomes in scenarios that have not been tested experimentally, making them an important resource in experimental and clinical pharmacology. However, these models are complicated to work with due to their size and inherent complexity. This makes many applications of QSP models for simulation, parameter estimation and trial design computationally intractable. A number of techniques have been developed to simplify QSP models into smaller models that are more amenable to further analyses while retaining their accurate predictive capabilities. Different simplification techniques have different strengths and weaknesses and hence different utilities. Understanding the utilities of different methods is essential for selection of the best method for a particular situation. In this paper, we have created an overall framework for model simplification techniques that allows a natural categorisation of methods based on their utility. We provide a brief description of the concept underpinning the different methods and example applications. A summary of the utilities of methods is intended to provide a guide to modellers in their model endeavours to simplify these complicated models.

3D human liver spheroids for translational pharmacology and toxicology.

Drug development is a failure-prone endeavour, and more than 85% of drugs fail during clinical development, showcasing that current preclinical systems for compound selection are clearly inadequate. Liver toxicity remains a major reason for safety failures. Furthermore, all efforts to develop pharmacological therapies for a variety of chronic liver diseases, such as non-alcoholic steatohepatitis (NASH) and fibrosis, remain unsuccessful. Considering the time and expense of clinical trials, as well as the substantial burden on patients, new strategies are thus of paramount importance to increase clinical success rates. To this end, human liver spheroids are becoming increasingly utilized as they allow to preserve patient-specific phenotypes and functions for multiple weeks in culture. We here review the recent application of such systems for i) predictive and mechanistic analyses of drug hepatotoxicity, ii) the evaluation of hepatic disposition and metabolite formation of low clearance drugs and iii) the development of drugs for metabolic and infectious liver diseases, including NASH, fibrosis, malaria and viral hepatitis. We envision that with increasing dissemination, liver spheroids might become the new gold standard for such applications in translational pharmacology and toxicology.

Management of Muscle Spasms in Adult Patients with Cerebral Palsy.

As medical care advances, there is a growing number of adult patients with cerebral palsy. The spastic form is characterized by muscle hypertonicity, hyperreflexia, and spasticity, which are associated with worse quality of life, poor functionality, and pain. This literature review attempts to explore the existing treatments for spasticity in cerebral palsy to provide insight into potential treatments in the adult population. The types of treatments are broadly categorized into physical therapy, pharmacologic treatments, botulinum toxin, surgical treatments, and alternative options.

Investigation on the mechanisms of guiqi huoxue capsule for treating cervical spondylosis based on network pharmacology and molecular docking.

BACKGROUND: Guiqi huoxue capsule (GQHXC) is a patented Chinese medicine used for treating a liver and kidney deficiency and blood stasis syndrome due to qi deficiency. It is caused by cervical spondylosis (cervical spondylotic radiculopathy (CSR), mixed cervical spondylosis mainly composed of nerve root type). Its underlying mechanisms need, however, to be further clarified. METHODS: In this study, collecting compounds, predicting therapeutic targets, constructing networks, and analyzing biological functions and pathways were based on network pharmacology analysis. In addition, molecular docking verification was engaged to assess the binding potential of selected target-compound pairs. RESULTS: We established 5 networks: compound-putative target network of GQHXC, protein-protein interaction (PPI) network related to CSR, compound-CSR target network, potential therapeutic targets PPI network, and herb-compound-target-pathway network. Network analysis indicated that 7 targets (tumor necrosis factor [TNF], interleukin 6 [IL6], nitric oxide synthase 3 [NOS3], Interleukin-8 [CXCL8], prostaglandin-endoperoxide synthase 2 [PTGS2], vascular endothelial growth factor A [VEGFA], and AP-1 transcription factor subunit [JUN]) might be the therapeutic targets of GQHXC in CSR. Moreover, molecular docking verification showed that TNF, IL6, NOS3, CXCL8, PTGS2, VEGFA, and JUN had a good is interaction with the corresponding compounds. Furthermore, enrichment analysis indicated that GQHXC might exert a curative role in CSR by regulating some important pathways, such as TNF signaling pathway, NF-kappa B signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and so on. CONCLUSION: Our study preliminarily explained the underlying mechanisms of GQHXC for treating CSR, and molecular docking verification was adopted as an additional verification. These findings laid a valuable foundation for experimental research and further application of GQHXC in the clinical treatment of CSR.

Mechanisms and molecular targets of the Yu-Ping-Feng powder for allergic rhinitis, based on network pharmacology.

ABSTRACT: In traditional Chinese medicine (TCM), Yu-Ping-Feng powder (YPFP) has been used to treat allergic rhinitis (AR) for centuries. However, the mechanisms underlying its effects or its molecular targets in AR treatment are yet to be elucidated. Therefore, the active compounds of YPFP and their targets were collected and identified from the Traditional Chinese Medicine Systems Pharmacology database. Moreover, AR-associated targets were acquired from the GeneCards and Online Mendelian Inheritance in Man database. Proteins interactions network of YPFP presumed targets and AR-associated targets were examined and merged to reveal the candidate YPFP targets against AR.Cytoscape software and BisoGenet Database were employed to perform the Visualization and Integrated Discovery (Cluster Profiler R package, version: 3.8.1). Kyoto Encyclopedia of Genes and Genomes and genome pathway analyses. To identify the key target genes, a gene-pathway network has been constructed.We identified 44 effective active compounds and 622 YPFP targets. Also 1324 target genes related to AR were identified. Twenty pathways, including those of AGE-RAGE signaling, fluid shear stress, atherosclerosis, PI3K-Akt signaling, and tumor necrosis factor signaling was enriched significantly. MAPK1 was identified as the core gene, while others including RELA, AKT1, NFKBIA, IL6, and JUN, were also important in the gene-pathway network. Clearly, network pharmacology can be applied in revealing the molecular targets and mechanisms of action of complex herbal preparations.These findings suggested that YPFP could treat AR by regulating immunological functions, diminishing inflammation, and improving immunity through different pathways.

The Problems of Applying Classical Pharmacology Analysis to Modern In Vitro Drug Discovery Assays: Slow Binding Kinetics and High Target Concentration.

The analysis framework used to quantify drug potency in vitro (e.g., Kd or Ki) was initially developed for classical pharmacology bioassays, for example, organ bath experiments testing moderate-affinity natural products. Modern drug discovery can infringe the assumptions of the classical pharmacology analysis equations, owing to the reduction of assay volume in miniaturization, target overexpression, and the increase of compound-target affinity in medicinal chemistry. These assumptions are that (1) the compound concentration greatly exceeds the target concentration (i.e., minimal ligand depletion), and (2) the compound is at equilibrium with the receptor (i.e., rapid ligand binding kinetics). Unappreciated infringement of these assumptions can lead to substantial underestimation of compound affinity, which negatively impacts the drug discovery process, from early-stage lead optimization to prediction of human dosing. This study evaluates the real-world impact of these factors on the target interaction assays used in drug discovery using literature examples, database searches, and simulations. The ranges of compound affinity and the assay types that are prone to depletion and equilibration artifacts are identified. Importantly, the highest-affinity compounds, usually the highest value chemical matter in drug discovery, are the most affected. Methods and simulation tools are provided to enable investigators to evaluate, manage, and minimize depletion or equilibration artifacts. This study enables the correct application of pharmacological data analysis to accurately quantify affinity using modern drug discovery assay technology.

Study on Mechanism of Ginkgo biloba L. Leaves for the Treatment of Neurodegenerative Diseases Based on Network Pharmacology.

Ginkgo biloba L. leaves (GBLs), as widely used plant extract sources, significantly improve cognitive, learning and memory function in patients with dementia. However, few studies have been conducted on the specific mechanism of Neurodegenerative diseases (NDs). In this study, network pharmacology was employed to elucidate potential mechanism of GBLs in the treatment of NDs. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to obtain the chemical components in accordance with the screening principles of oral availability and drug-like property. Potential targets of GBLs were integrated with disease targets, and intersection targets were exactly the potential action targets of GBLs for treating NDs; these key targets were enriched and analyzed by the protein protein interaction (PPI) analysis and molecular docking verification. Key genes were ultimately used to find the biological pathway and explain the therapeutic mechanism by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Twenty-seven active components of GBLs may affect biological processes such as oxidative reactions and activate transcription factor activities. These components may also affect 120 metabolic pathways, such as the PI3K/AKT pathway, by regulating 147 targets, including AKT1, ALB, HSP90AA1, PTGS2, MMP9, EGFR and APP. By using the software iGEMDOCK, the main target proteins were found to bind well to the main active components of GBLs. GBLs have the characteristics of multi-component and multi-target synergistic effect on the treatment of NDs, which preliminarily predicted its possible molecular mechanism of action, and provided the basis for the follow-up study.

Developing Tools to Evaluate Non-linear Mixed Effect Models: 20 Years on the npde Adventure.

This article revisits 20 years of our work in developing evaluation tools adapted to non-linear mixed effect models. These hierarchical models involve a large number of assumptions concerning the structural evolution of the outcomes, the link between different outcomes, the variabilities in the parameters and model evaluation aims at assessing these various components, both to help guide the model building and to communicate on model adequacy for a given purpose. During our career, we have developed and extended simulation-based evaluation tools called normalised prediction discrepancies (npd) and normalised prediction distribution errors (npde), providing informative diagnostics through graphs and tests.

Network pharmacology integrated with molecular docking reveals the common experiment-validated antipyretic mechanism of bitter-cold herbs.

ETHNOPHARMACOLOGICAL RELEVANCE: Bitter-cold herbs have been used to clearing heat and expelling damp in clinical practice in China for thousands of years. AIM OF THE STUDY: This study aimed to investigate the common molecular mechanism of bitter-cold herbs through network pharmacology analysis, molecular docking and experimental validation in vivo. MATERIALS AND METHODS: Network pharmacological analysis integrated with molecular docking was employed to identify the active compounds and core action targets of the bitter-cold herbs. Then, the yeast-induced pathological model was established, and the antipyretic effect of the herbs was evaluated by checking rectal temperatures of the mice hourly. Lastly, the protein expression of core targets was examined to reveal the antipyretic mechanism. RESULTS: A total of 52 lead compounds from the four bitter-cold herbs, Phellodendri Chinensis Cortex (PCC), Sophorae Flavescentis Radix (SFR), Gentianae Radix Et Rhozima (GRER) and Coptidis Rhizoma (CR), and 248 compounds-related targets were screened out with PTGS2 ranking the first. The results from molecular docking showed that 22 compounds adopted the same orientation as aspirin and had an excellent stability in the active site pocket of PTGS2. Furthermore, these herbs exerted potential therapeutic effects through 38 related pathways. On the other hand, the outcome of animal experiments showed that they could significantly attenuate the yeast-induced mice fever with dose-dependent relationship. Further experimental results demonstrated that administration of yeast suspension raised protein expression of PTGS2 significantly, which was evidently inhibited in the high or low-dose groups of GRER as well as in the low-dose group of SFR (P < 0.01) though a higher expression of PTGS2 was shown in the low-dose group of CR compared with FM group (P < 0.01). CONCLUSIONS: The bitter-cold herbs can alleviate fever response and their antipyretic effect may mainly be attributed to regulating the expression of PTGS2 after the formation of ligand-receptor/PTGS2 complexes, and their active compounds might be nominated as antipyretic lead-ligand candidates.

A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC.

ETHNOPHARMACOLOGICAL RELEVANCE: Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied. AIM OF STUDY: A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC. MATERIALS AND METHODS: Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats. RESULTS: Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro. CONCLUSION: The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.

Reduction of quantitative systems pharmacology models using artificial neural networks.

Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output relationship. In this study, we explore the use of artificial neural networks for approximating an input-output relationship within highly dimensional systems models. We illustrate this approach using a model of blood coagulation. The model consists of two components linked together through a highly dimensional discontinuous interface, which creates a difficulty for model reduction techniques. The proposed approach enables the development of an efficient approximation to complex models with the desired level of accuracy. The technique is applicable to a wide variety of models and provides substantial speed boost for use of such models in simulation and control purposes.