[Modular pharmacology: research progress and development of analysis platform].

Based on the systematic deconstruction of multi-dimensional and multi-target biological networks, modular pharmacology explains the complex mechanism of diseases and the interactions of multi-target drugs. It has made progress in the fields of pathogenesis of disease, biological basis of disease and traditional Chinese medicine(TCM) syndrome, pharmacological mechanism of multi-target herbs, compatibility of formulas, and discovery of new drug of TCM compound. However, the complexity of multi-omics data and biological networks brings challenges to the modular deconstruction and analysis of the drug networks. Here, we constructed the "Computing Platform for Modular Pharmacology" online analysis system, which can implement the function of network construction, module identification, module discriminant analysis, hub-module analysis, intra-module and inter-module relationship analysis, and topological visualization of network based on quantitative expression profiles and protein-protein interaction(PPI) data. This tool provides a powerful tool for the research on complex diseases and multi-target drug mechanisms by means of modular pharmacology. The platform may have broad range of application in disease modular identification and correlation mechanism, interpretation of scientific principles of TCM, analysis of complex mechanisms of TCM and formulas, and discovery of multi-target drugs.

Of rodents, research and relationships: a pharmacological odyssey.

This article is an autobiographical account of a research career in inflammatory diseases, mechanisms and pharmacotherapy, drug research and development, in academia and industry in various European countries spanning the last 55 years. The author describes how tenacity and independent thought, learned in formative years, and tempered later by the development of good relationships with colleagues have guided his career. This has spanned research, among other fields, on prostaglandins as pro-and anti-inflammatory mediators, oxidative stress and antioxidants, phospholipid mediators, cytokines, innate and adaptive immune responses and the establishment of various inflammatory and immunological models. The author has helped discover and develop novel therapeutic approaches to pain, arthritic, dermatological, respiratory, and autoimmune disorders and contributed to bringing eight drug candidates to clinical trials. He has helped establish new research labs in four different centres and been involved in teaching undergraduate and mature students in three different universities. With extensive experience in scientific publishing and several international awards, he emphasises that without good teamwork, little can be achieved in scientific research.

[Fluorescence microscopy for brain activity imaging: one-photon microscopy and its application to pharmacological research].

The development of genetically-encoded fluorescent probes for the detection of intracellular calcium ions and various neurotransmitters has progressed significantly in recent years, and there is a growing need for techniques that rapidly and efficiently image these signals in the living brain for pharmacological studies of the central nervous system. In this article, we discuss one-photon fluorescence microscopy techniques used for brain activity imaging, particularly wide-field imaging and head-mounted miniaturized microscopy, and introduce their basic principles, recent advances, and applications in pharmacological research. Wide-field calcium imaging is suitable for mesoscopic observation of cortical activity during behavioral tasks in head-fixed awake mice, while head-mounted miniaturized microscopes can be attached to the animal's head to image brain activity associated with naturalistic behaviors such as social behavior and sleep. One-photon microscopy allows for the development of a simple and cost-effective imaging system using an affordable excitation light source such as a light-emitting diode. Its excitation light illuminates the entire field of view simultaneously, making it easy to perform high-speed imaging using a high-sensitivity camera. In contrast, the short wavelength of the excitation light limits the field of observation to areas on or near the brain surface due to its strong light scattering. Moreover, the out-of-focus fluorescence makes it difficult to obtain images with a high signal-to-noise ratio and spatial resolution. The use of one-photon microscopy in brain activity imaging has been limited compared to two-photon microscopy, but its advantages have recently been revisited. Therefore, this technique is expected to become a useful method for pharmacologists to visualize the activity of the living brain.

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

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.

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.

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.

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.

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.