Current practices for QSP model assessment: an IQ consortium survey

Future Directions for Quantitative Systems Pharmacology

In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI. In preclinical development, QSP will integrate with non-animal "new approach methodologies" and reverse-translated datasets to improve understanding of and translation to the human patient. During clinical development, integration with complementary modeling approaches and multimodal patient data will create multidimensional digital twins and virtual populations for clinical trial simulations that guide clinical development and point to opportunities for precision medicine. QSP can evolve into this future by (1) pursuing high-impact applications enabled by novel experimental and quantitative technologies and data types; (2) integrating closely with analytical and computational advancements; and (3) increasing efficiencies through automation, standardization, and model reuse. In this vision, the QSP expert will play a critical role in designing strategies, evaluating data, staging and executing analyses, verifying, interpreting, and communicating findings, and ensuring the ethical, safe, and rational application of novel data types, technologies, and advanced analytics including AI/ML.

Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers

Neuroendocrine tumors (NETs) occur sporadically or as part of rare endocrine tumor syndromes (RETSs) such as multiple endocrine neoplasia 1 and von Hippel-Lindau syndromes. Due to their relative rarity and lack of model systems, NETs and RETSs are difficult to study, hindering advancements in therapeutic development. Causal or mechanistic mathematical modeling is widely deployed in disease areas such as breast and prostate cancers, aiding the understanding of observations and streamlining in vitro and in vivo modeling efforts. Mathematical modeling, while not yet widely utilized in NET research, offers an opportunity to accelerate NET research and therapy development. To illustrate this, we highlight examples of how mathematical modeling associated with more common endocrine cancers has been successfully used in the preclinical, translational and clinical settings. We also provide a scope of the limited work that has been done in NETs and map how these techniques can be utilized in NET research to address specific outstanding challenges in the field. Finally, we include practical details such as hardware and data requirements, present advantages and disadvantages of various mathematical modeling approaches and discuss challenges of using mathematical modeling. Through a cross-disciplinary approach, we believe that many currently difficult problems can be made more tractable by applying mathematical modeling and that the field of rare diseases in endocrine oncology is well poised to take advantage of these techniques.

What Is QSP and Why Does It Exist?: A Brief History

Quantitative systems pharmacology (QSP) is a modeling approach employed in drug research and development that combines mechanistic representations of biological processes with drug pharmacology to deepen biological understanding and predict the responses to novel drugs or protocols. QSP has evolved from and is related to other modeling approaches, but has a number of unique attributes and applications. Here, we clarify the definition of QSP and its key features, trace its evolution, briefly compare it to other approaches, and explain why and how it can be used to reduce risk and improve efficiency in drug research and development.

Application of Quantitative Systems Pharmacology Approaches to Support Pediatric Labeling in Rare Diseases

Quantitative Systems Pharmacology (QSP) models offer a promising approach to extrapolate drug efficacy across different patient populations, particularly in rare diseases. Unlike conventional empirical models, QSP models can provide a mechanistic understanding of disease progression and therapeutic response by incorporating current disease knowledge into the descriptions of biomarkers and clinical endpoints. This allows for a holistic representation of the disease and drug response. The mechanistic nature of QSP models is well suited to pediatric extrapolation concepts, providing a quantitative method to assess disease and drug response similarity between adults and pediatric patients. The application of a QSP-based assessment of the disease and drug similarity in adult and pediatric patients in the clinical development program of olipudase alfa, a treatment for Acid Sphingomyelinase Deficiency (ASMD), illustrates the potential of this approach.

Application of MIDD to accelerate the development of anti-infectives: Current status and future perspectives

This review examines the role of model-informed drug development (MIDD) in advancing antibacterial and antiviral drug development, with an emphasis on the inclusion of host system dynamics into modeling efforts. Amidst the growing challenges of multidrug resistance and diminishing market returns, innovative methodologies are crucial for continuous drug discovery and development. The MIDD approach, with its robust capacity to integrate diverse data types, offers a promising solution. In particular, the utilization of appropriate modeling and simulation techniques for better characterization and early assessment of drug resistance are discussed. The evolution of MIDD practices across different infectious disease fields is also summarized, and compared to advancements achieved in oncology. Moving forward, the application of MIDD should expand into host system dynamics as these considerations are critical for the development of "live drugs" (e.g. chimeric antigen receptor T cells or bacteriophages) to address issues like antibiotic resistance or latent viral infections.

An industry perspective on current QSP trends in drug development

2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

Comparison of drug-induced liver injury risk between propylthiouracil and methimazole: A quantitative systems toxicology approach

Propylthiouracil (PTU) and methimazole (MMI), two classical antithyroid agents possess risk of drug-induced liver injury (DILI) with unknown mechanism of action. This study aimed to examine and compare their hepatic toxicity using a quantitative system toxicology approach. The impact of PTU and MMI on hepatocyte survival, oxidative stress, mitochondrial function and bile acid transporters were assessed in vitro. The physiologically based pharmacokinetic (PBPK) models of PTU and MMI were constructed while their risk of DILI was calculated by DILIsym, a quantitative systems toxicology (QST) model by integrating the results from in vitro toxicological studies and PBPK models. The simulated DILI (ALT >2 × ULN) incidence for PTU (300 mg/d) was 21.2%, which was within the range observed in clinical practice. Moreover, a threshold dose of 200 mg/d was predicted with oxidative stress proposed as an important toxic mechanism. However, DILIsym predicted a 0% incidence of hepatoxicity caused by MMI (30 mg/d), suggesting that the toxicity of MMI was not mediated through mechanism incorporated into DILIsym. In conclusion, DILIsym appears to be a practical tool to unveil hepatoxicity mechanism and predict clinical risk of DILI.

Quantitative systems pharmacology Model to characterize valproic acid-induced hyperammonemia and the effect of L-carnitine supplementation

Valproic acid (VPA) is a short-chain fatty acid widely prescribed in the treatment of seizure disorders and epilepsy syndromes, although its therapeutic value may be undermined by its toxicity. VPA serious adverse effects are reported to have a significant and dose-dependent incidence, many associated with VPA-induced hyperammonemia. This effect has been linked with reduced levels of carnitine; an endogenous compound involved in fatty acid's mitochondrial β-oxidation by facilitation of its entrance via the carnitine shuttle. High exposure to VPA can lead to carnitine depletion causing a misbalance between the intra-mitochondrial β-oxidation and the microsomal ω-oxidation, a pathway that produces toxic metabolites such as 4-en-VPA which inhibits ammonia elimination. Moreover, a reduction in carnitine levels might be also related to VPA-induced obesity and lipids disorder. In turn, L-carnitine supplementation (CS) has been recommended and empirically used to reduce VPA's hepatotoxicity. The aim of this work was to develop a Quantitative Systems Pharmacology (QSP) model to characterize VPA-induced hyperammonemia and evaluate the benefits of CS in preventing hyperammonemia under both chronic treatment and after VPA overdosing. The QSP model included a VPA population pharmacokinetics model that allowed the prediction of total and unbound concentrations after single and multiple oral doses considering its saturable binding to plasma proteins. Predictions of time courses for 2-en-VPA, 4-en-DPA, VPA-glucuronide, carnitine, ammonia and urea levels, and for the relative change in fatty acids, Acetyl-CoA, and glutamate reflected the VPA induced changes and the efficacy of the treatment with L-carnitine. The QSP model was implemented to give a rational basis for the L-carnitine dose selection to optimize CS depending on VPA dosage regime and to assess the currently recommended L-carnitine rescue therapy after VPA overdosing. Results show that a L-carnitine dose equal to the double of the VPA dose using the same interdose interval would maintain the ammonia levels at baseline. The QSP model may be expanded in the future to describe other adverse events linked to VPA-induced changes in endogenous compounds.