A robust computational pipeline for model-based and data-driven phenotype clustering.

MOTIVATION: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Role of Disease Progression Models in Drug Development.

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy.

Pregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted. Part of the challenge is the uncertain risk potential that pregnant women represent in the absence of timely and costly toxicology and developmental pharmacology studies, which only partly mitigate such risks. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed. Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Funding for translational research in pregnancy is limited but will fill some of these gaps, especially when joined with ongoing clinical trials in pregnancy that also fill certain knowledge gaps, especially biomarker and end point evaluation across pregnancy states linked to clinical outcomes. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real-world data sources and complimentary artificial intelligence/machine learning approaches. The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.

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.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis.

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Efficacy and tolerability of an endogenous metabolic modulator (AXA1125) in fatigue-predominant long COVID: a single-centre, double-blind, randomised controlled phase 2a pilot study.

Background: 'Long COVID' describes persistent symptoms, commonly fatigue, lasting beyond 12 weeks following SARS-CoV-2 infection. Potential causes include reduced mitochondrial function and cellular bioenergetics. AXA1125 has previously increased ß-oxidation and improved bioenergetics in preclinical models along with certain clinical conditions, and therefore may reduce fatigue associated with Long COVID. We aimed to assess the efficacy, safety and tolerability of AXA1125 in Long COVID. Methods: Patients with fatigue-dominant Long COVID were recruited in this single-centre, double-blind, randomised controlled phase 2a pilot study completed in the UK. Patients were randomly assigned (1:1) using an Interactive Response Technology to receive either AXA1125 or matching placebo in a clinical-based setting. Each dose (33.9 g) of AXA1125 or placebo was administered orally in a liquid suspension twice daily for four weeks with a two-week follow-up period. The primary endpoint was the mean change from baseline to day 28 in the phosphocreatine (PCr) recovery rate following moderate exercise, assessed by 31P-magnetic resonance spectroscopy (MRS). All patients were included in the intention to treat analysis. This trial was registered at ClinicalTrials.gov, NCT05152849. Findings: Between December 15th 2021, and May 23th 2022, 60 participants were screened, and 41 participants were randomised and included in the final analysis. Changes in skeletal muscle phosphocreatine recovery time constant (τPCr) and 6-min walk test (6MWT) did not significantly differ between treatment (n = 21) and placebo group (n = 20). However, treatment with AXA1125 was associated with significantly reduced day 28 Chalder Fatigue Questionnaire [CFQ-11] fatigue score when compared with placebo (least squares mean difference [LSMD] -4.30, 95% confidence interval (95% CI) -7.14, -1.47; P = 0.0039). Eleven (52.4%, AXA1125) and four (20.0%, placebo) patients reported treatment-emergent adverse events; none were serious or led to treatment discontinuation. Interpretation: Although treatment with AXA1125 did not improve the primary endpoint (τPCr-measure of mitochondrial respiration), when compared to placebo, there were significant improvements in fatigue-based symptoms among patients living with Long COVID following a four-week treatment period. Further multicentre studies are needed to validate our findings in a larger cohort of patients with fatigue-dominant Long COVID. Funding: Axcella Therapeutics.

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development.

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model-informed drug discovery and development approach can leverage adult-pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage-gate decisions informed by such an approach.

Quantifying cholesterol synthesis in vivo using (2)H(2)O: enabling back-to-back studies in the same subject.

The advantages of using (2)H(2)O to quantify cholesterol synthesis include i) homogeneous precursor labeling, ii) incorporation of (2)H via multiple pathways, and iii) the ability to perform long-term studies in free-living subjects. However, there are two concerns. First, the t(1/2) of tracer in body water presents a challenge when there is a need to acutely replicate measurements in the same subject. Second, assumptions are made regarding the number of hydrogens (n) that are incorporated during de novo synthesis. Our primary objective was to determine whether a step-based approach could be used to repeatedly study cholesterol synthesis a subject. We observed comparable changes in the (2)H-labeling of plasma water and total plasma cholesterol in African-Green monkeys that received five oral doses of (2)H(2)O, each dose separated by one week. Similar rates of cholesterol synthesis were estimated when comparing data in the group over the different weeks, but better reproducibility was observed when comparing replicate determinations of cholesterol synthesis in the same nonhuman primate during the respective dosing periods. Our secondary objective was to determine whether n depends on nutritional status in vivo; we observed n of ∼25 and ∼27 in mice fed a high-carbohydrate (HC) versus carbohydrate-free (CF) diet, respectively. We conclude that it is possible to acutely repeat studies of cholesterol synthesis using (2)H(2)O and that n is relatively constant.

Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis.

Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.

QSPcc reduces bottlenecks in computational model simulations.

Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances.

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 Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat.

Gaucher's disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first-line SRT approved for GD1, on treatment-naïve or patients with ERT-stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype-phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology.

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.

Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative Systems Pharmacology Simulators.

Quantitative Systems Pharmacology (QSP) modeling is increasingly used as a quantitative tool for advancing mechanistic hypotheses on the mechanism of action of a drug, and its pharmacological effect in relevant disease phenotypes, to enable linking the right drug to the right patient. Application of QSP models relies on creation of virtual populations for simulating scenarios of interest. Creation of virtual populations requires 2 important steps, namely, identification of a subset of model parameters that can be associated with a phenotype of disease and development of a sampling strategy from identified distributions of these parameters. We improve on existing sampling methodologies by providing a means of representing the structural relationship across model parameters and describing propagation of variability in the model. This gives a robust, systematic method for creating a virtual population. We have developed the Linear-In-Flux-Expressions (LIFE) method to simulate variability in patient pharmacokinetics and pharmacodynamics using relationships between parameters at baseline to create a virtual population. We demonstrate the importance of this methodology on a model of cholesterol metabolism. The LIFE methodology brings us a step closer toward improved QSP simulators through enhanced capture of the observed variability in drug and disease clinical data.

A Quantitative Systems Pharmacology Platform to Investigate the Impact of Alirocumab and Cholesterol-Lowering Therapies on Lipid Profiles and Plaque Characteristics.

Reduction in low-density lipoprotein cholesterol (LDL-C) is associated with decreased risk for cardiovascular disease. Alirocumab, an antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly reduces LDL-C. Here, we report development of a quantitative systems pharmacology (QSP) model integrating peripheral and liver cholesterol metabolism, as well as PCSK9 function, to examine the mechanisms of action of alirocumab and other lipid-lowering therapies, including statins. The model predicts changes in LDL-C and other lipids that are consistent with effects observed in clinical trials of single or combined treatments of alirocumab and other treatments. An exploratory model to examine the effects of lipid levels on plaque dynamics was also developed. The QSP platform, on further development and qualification, may support dose optimization and clinical trial design for PCSK9 inhibitors and lipid-modulating drugs. It may also improve our understanding of factors affecting therapeutic responses in different phenotypes of dyslipidemia and cardiovascular disease.

A cell-sparing electric field stimulation technique for high-throughput screening of voltage-gated ion channels.

The Trans Cell Layer Electrical Field Stimulation (TCL-EFS) system has been developed for high-throughput screening (HTS) of voltage-gated ion channels in microplate format on a Voltage-Ion Probe Reader (VIPR) platform. In this design, a wire electrode is placed above the cell layer of each filter well, and a whole plate perimeter electrode resides beneath the filter layer. This configuration allows the electrodes to be placed away from the cell layer to minimize the near electrode field effects on cell function and dye bleaching observed with other existing designs. Mathematical simulation indicates that the electric field at the cell layer becomes uniform as the top electrode is raised to a position near the surface of the solution in the well. Using the TCL-EFS system and membrane potential fluorescence resonance energy transfer (FRET) dyes, the sensitivity of voltage-gated sodium channels to tetrodotoxin and other channel inhibitors was found to be similar to those determined by established electrophysiological and more conventional VIPR techniques. A good correlation was also observed with the TCL-EFS system for inhibition of Cav2.2 by omega-conotoxin-GVIA and for block of Cav1.2 by known small molecule inhibitors. Thus, the TCLEFS system is suitable for both quantitative analysis and HTS of voltage-gated sodium and calcium channels, without the liabilities of previously reported EFS methodologies.

Discovery and Clinical Evaluation of MK-8150, A Novel Nitric Oxide Donor With a Unique Mechanism of Nitric Oxide Release.

BACKGROUND: Nitric oxide donors are widely used to treat cardiovascular disease, but their major limitation is the development of tolerance, a multifactorial process to which the in vivo release of nitric oxide is thought to contribute. Here we describe the preclinical and clinical results of a translational drug development effort to create a next-generation nitric oxide donor with improved pharmacokinetic properties and a unique mechanism of nitric oxide release through CYP3A4 metabolism that was designed to circumvent the development of tolerance. METHODS AND RESULTS: Single- and multiple-dose studies in telemetered dogs showed that MK-8150 induced robust blood-pressure lowering that was sustained over 14 days. The molecule was safe and well tolerated in humans, and single doses reduced systolic blood pressure by 5 to 20 mm Hg in hypertensive patients. Multiple-dose studies in hypertensive patients showed that the blood-pressure-lowering effect diminished after 10 days, and 28-day studies showed that the hemodynamic effects were completely lost by day 28, even when the dose of MK-8150 was increased during the dosing period. CONCLUSIONS: The novel nitric oxide donor MK-8150 induced significant blood-pressure lowering in dogs and humans for up to 14 days. However, despite a unique mechanism of nitric oxide release mediated by CYP3A4 metabolism, tolerance developed over 28 days, suggesting that tolerance to nitric oxide donors is multifactorial and cannot be overcome solely through altered in vivo release of nitric oxide. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifiers: NCT01590810 and NCT01656408.

A novel 3-dimensional micro-ultrasound approach to automated measurement of carotid arterial plaque volume as a biomarker for experimental atherosclerosis.

Improved methods for non-invasive in vivo assessment are needed to guide development of animal models of atherosclerosis and to evaluate target engagement and in vivo efficacy of new drugs. Using novel 3D-micro-ultrasound technology, we developed and validated a novel protocol for 3D acquisition and analysis of imaging to follow lesion progression in atherosclerotic mice. The carotid arteries of ApoE receptor knockout mice and normal control mice were imaged within the proximal 2mm from the aortic branch point. Plaque volume along that length was quantified using a semi-automated 3D segmentation algorithm. Volumes derived by this method were compared to those calculated using 3-D histology post-mortem. Bland-Altman comparison revealed close correlation between these two measures of plaque volume. Furthermore, using a segmentation technique that captures early positive and 33 week negative remodeling, we found evidence that plaque volume increases linearly over time. Each animal and each plaque served as its own control, allowing accurate comparison. The high fidelity anatomical registration of this protocol provides increased spatial resolution and therefore greater sensitivity for measurement of plaque wall size, an advance over 2-dimensional measures of intimal-medial-thickening. Further, 3-dimensional analysis ensures a point of registration that captures functional markers in addition to the standard structural markers that characterize experimental atherosclerosis. In conclusion, this novel imaging protocol provides a non-invasive, accurate surrogate marker for experimental atherosclerosis over the life of the entire lesion.

Segmentation of arterial vessel wall motion to sub-pixel resolution using M-mode ultrasound.

We describe a method for segmenting arterial vessel wall motion to sub-pixel resolution, using the returns from M-mode ultrasound. The technique involves measuring the spatial offset between all pairs of scans from their cross-correlation, converting the spatial offsets to relative wall motion through a global optimization, and finally translating from relative to absolute wall motion by interpolation over the M-mode image. The resulting detailed wall distension waveform has the potential to enhance existing vascular biomarkers, such as strain and compliance, as well as enable new ones.

A one-dimensional model of blood flow in arteries with friction and convection based on the Womersley velocity profile.

In this paper, we present a one-dimensional model for blood flow in arteries, without assuming an a priori shape for the velocity profile across an artery (Azer, Ph.D. thesis, Courant Institute, New York University, 2006). We combine the one-dimensional equations for conservation of mass and momentum with the Womersley model for the velocity profile in an iterative way. The pressure gradient of the one-dimensional model drives the Womersley equations, and the velocity profiles calculated then feed back into both the friction and nonlinear parts of the one-dimensional model. Besides enabling us to evaluate the friction correctly and also to use the velocity profile to correct the nonlinear terms, having the velocity profile available as output should be useful in a variety of applications. We present flow simulations using both structured trees and pure resistance models for the small arteries, and compare the resulting flow and pressure waves under various friction models. Moreover, we show how to couple the one-dimensional equations with the Taylor diffusion limit (Azer, Int J Heat Mass Transfer 2005;48:2735-40; Taylor, Proc R Soc Lond Ser A 1953;219:186-203) of the convection-diffusion equations to drive the concentration of a solute along an artery in time.