Using quantitative systems pharmacology to evaluate the drug efficacy of COX-2 and 5-LOX inhibitors in therapeutic situations

A pipeline for testing drug mechanism of action and combination therapies: From microarray data to simulations via Linear-In-Flux-Expressions: Testing four-drug combinations for tuberculosis treatment

Computational methods are becoming commonly used in many areas of medical research. Recently, the modeling of biological mechanisms associated with disease pathophysiology have benefited from approaches such as Quantitative Systems Pharmacology (briefly QSP) and Physiologically Based Pharmacokinetics (briefly PBPK). These methodologies show the potential to enhance, if not substitute animal models. The main reasons for this success are the high accuracy and low cost. Solid mathematical foundations of such methods, such as compartmental systems and flux balance analysis, provide a good base on which to build computational tools. However, there are many choices to be made in model design, that will have a large impact on how these methods perform as we scale up the network or perturb the system to uncover the mechanisms of action of new compounds or therapy combinations. A computational pipeline is presented here that starts with available -omic data and utilizes advanced mathematical simulations to inform the modeling of a biochemical system. Specific attention is devoted to creating a modular workflow, including the mathematical rigorous tools to represent complex chemical reactions, and modeling drug action in terms of its impact on multiple pathways. An application to optimizing combination therapy for tuberculosis shows the potential of the approach.

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.

Regulation of Drug Transport Proteins-From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post‐translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation‐mediated changes in transporter function on drug disposition and response.

Licofelone, a potent COX/5-LOX inhibitor and a novel option for treatment of neurological disorders

Neurological disorders result in disability and morbidity. Neuroinflammation is a key factor involved in progression or resolution of a series of neurological disorders like Huntington disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD), Spinal Cord Injury (SCI), and Seizure. Thereby, anti-inflammatory drugs have been developed to improve the neurodegenerative impairments. Licofelone is an approved osteoarthritis drug that inhibits both the COX (cyclooxygenase) and 5-LOX (lipoxygenase) pathways. Licofelone has pain-relieving and anti-inflammatory effects and it was shown to have neuroprotective properties in the central nervous system, which is implicated in its regulatory effect on the COX/5-LOX pathway, inflammatory cytokines, and immune responses. In this study, we briefly review the various features of neurological disorders and the function of COX/LOX in their flare up and current pharmacological products for their management. Moreover, this review attempts to summarize potential therapeutics that target the immune responses within the central nervous system. A better understanding of the interactions between Licofelone and the nervous systems will be crucial to demonstrate the possible efficacy of Licofelone in neurological disorders.

A Model-Based Workflow to Benchmark the Clinical Cholestasis Risk of Drugs

We present a generic workflow combining physiology-based computational modeling and in vitro data to assess the clinical cholestatic risk of different drugs systematically. Changes in expression levels of genes involved in the enterohepatic circulation of bile acids were obtained from an in vitro assay mimicking 14 days of repeated drug administration for 10 marketed drugs. These changes in gene expression over time were contextualized in a physiology-based bile acid model of glycochenodeoxycholic acid. The simulated drug-induced response in bile acid concentrations was then scaled with the applied drug doses to calculate the cholestatic potential for each compound. A ranking of the cholestatic potential correlated very well with the clinical cholestasis risk obtained from medical literature. The proposed workflow allows benchmarking the cholestatic risk of novel drug candidates. We expect the application of our workflow to significantly contribute to the stratification of the cholestatic potential of new drugs and to support animal-free testing in future drug development.

Montelukast potentiates the antiinflammatory effect of NSAIDs in the rat paw formalin model and simultaneously minimizes the risk of gastric damage

OBJECTIVE AND DESIGN

Montelukast, a cysteinyl leukotriene receptor antagonist, exhibits antiinflammatory action. We tested whether exposure to montelukast plus nonsteroidal antiinflammatory drugs (NSAIDs) elicits better control of paw inflammation in the rat formalin test and improves associated gastric damage.

MATERIALS

A total of 46 adult male rats were used in the study.

TREATMENTS

We evaluated separate and combined effects of montelukast (20 mg/kg), celecoxib (COX2 inhibitor, 10 mg/kg), and diclofenac (nonselective COX1/COX2 inhibitor, 10 mg/kg) on paw and gastric damage in the rat formalin test.

RESULTS

Individual pretreatments of rats with montelukast, diclofenac, or celecoxib partly reduced formalin-induced increases in (i) paw edema, fibrosis, and inflammatory cells, (iii) serum interleukin-6 (IL-6) and leukotrienes (LTB4 and LTD4), and (iv) paw expressions of inducible nitric oxide synthase (iNOS) and COX2. These effects were accentuated in rats treated with montelukast plus diclofenac or montelukast plus celecoxib. Alternatively, montelukast or celecoxib, but not diclofenac, alleviated formalin-evoked gastric damage and increments in tumor necrosis factor-α and decrements in prostaglandin-E₂. These advantageous gastric influences were potentiated in rats treated with montelukast plus celecoxib.

CONCLUSIONS

While montelukast equally enhances antiinflammatory action of diclofenac or celecoxib via downregulating iNOS/COX2/LTs/IL-6 signaling, its gastroprotective action is preferentially potentiated by celecoxib.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

A workflow to build PBTK models for novel species

Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future.

Effect of Tumor Perfusion and Receptor Density on Tumor Control Probability in 177Lu-DOTATATE Therapy: An In Silico Analysis for Standard and Optimized Treatment

The aim of this work was to determine a minimal tumor perfusion and receptor density for ¹⁷⁷Lu-DOTATATE therapy using physiologically based pharmacokinetic (PBPK) modeling considering, first, a desired tumor control probability (TCP) of 99% and, second, a maximal tolerated biologically effective dose (BED_max) for organs at risk (OARs) in the treatment of neuroendocrine tumors and meningioma. Methods: A recently developed PBPK model was used. Nine virtual patients (i.e., individualized PBPK models) were used to perform simulations of pharmacokinetics for different combinations of perfusion (0.001-0.1 mL/g/min) and receptor density (1-100 nmol/L). The TCP for each combination was determined for 3 different treatment strategies: a standard treatment (4 cycles of 7.4 GBq and 105 nmol), a treatment maximizing the number of cycles based on BED_max for red marrow and kidneys, and a treatment having 4 cycles with optimized ligand amount and activity. The red marrow and the kidneys (BED_max of 2 Gy₁₅ and 40 Gy_2.5, respectively) were assumed to be OARs. Additionally, the influence of varying glomerular filtration rates, kidney somatostatin receptor densities, tumor volumes, and release rates was investigated. Results: To achieve a TCP of at least 99% in the standard treatment, a minimal tumor perfusion of 0.036 ± 0.023 mL/g/min and receptor density of 34 ± 20 nmol/L were determined for the 9 virtual patients. With optimization of the number of cycles, the minimum values for perfusion and receptor density were considerably lower, at 0.022 ± 0.012 mL/g/min and 21 ± 11 nmol/L, respectively. However, even better results (perfusion, 0.018 ± 0.009 mL/g/min; receptor density, 18 ± 10 nmol/L) were obtained for strategy 3. The release rate of ¹⁷⁷Lu (or labeled metabolites) from tumor cells had the strongest effect on the minimal perfusion and receptor density for standard and optimized treatments. Conclusion: PBPK modeling and simulations represent an elegant approach to individually determine the minimal tumor perfusion and minimal receptor density required to achieve an adequate TCP. This computational method can be used in the radiopharmaceutical development process for ligand and target selection for specific types of tumors. In addition, this method could be used to optimize clinical trials.

Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.

Quantitative systems pharmacology of interferon alpha administration: A multi-scale approach

The therapeutic effect of a drug is governed by its pharmacokinetics which determine the downstream pharmacodynamic response within the cellular network. A complete understanding of the drug-effect relationship therefore requires multi-scale models which integrate the properties of the different physiological scales. Computational modelling of these individual scales has been successfully established in the past. However, coupling of the scales remains challenging, although it will provide a unique possibility of mechanistic and holistic analyses of therapeutic outcomes for varied treatment scenarios. We present a methodology to combine whole-body physiologically-based pharmacokinetic (PBPK) models with mechanistic intracellular models of signal transduction in the liver for therapeutic proteins. To this end, we developed a whole-body distribution model of IFN-α in human and a detailed intracellular model of the JAK/STAT signalling cascade in hepatocytes and coupled them at the liver of the whole-body human model. This integrated model infers the time-resolved concentration of IFN-α arriving at the liver after intravenous injection while simultaneously estimates the effect of this dose on the intracellular signalling behaviour in the liver. In our multi-scale physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) model, receptor saturation is seen at low doses, thus giving mechanistic insights into the pharmacodynamic (PD) response. This model suggests a fourfold lower intracellular response after administration of a typical IFN-α dose to an individual as compared to the experimentally observed responses in in vitro setups. In conclusion, this work highlights clear differences between the observed in vitro and in vivo drug effects and provides important suggestions for future model-based study design.