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The selection of appropriate starting dose and suitable method to predict an efficacious dose for novel oncology drug in the early clinical development stage poses significant challenges. The traditional methods of using body surface area transformation from toxicology studies to predict the first-in human (FIH) starting dose, or simply selecting the maximum tolerated dose (MTD) or maximum administered dose (MAD) as efficacious dose or recommended phase 2 dose (RP2D), are usually inadequate and risky for novel oncology drugs.Due to the regulatory efforts aimed at improving dose optimisation in oncology drug development, clinical dose selection is now shifting away from these traditional methods towards a comprehensive benefit/risk assessment-based approach. Quantitative pharmacology analysis (QPA) plays a crucial role in this new paradigm. This mini-review summarises the use of QPA in selecting the starting dose for oncology FIH studies and potential efficacious doses for expansion or phase 2 trials. QPA allows for a more rational and scientifically based approach to dose selection by integrating information across studies and development phases.In conclusion, the application of QPA in oncology drug development has the potential to significantly enhance the success rates of clinical trials and ultimately support clinical decision-making, particularly in dose selection.

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Non-Small Cell Lung Cancer (NSCLC) remains one of the main causes of cancer death worldwide. In the urge of finding an effective approach to treat cancer, enormous therapeutic targets and treatment combinations are explored in clinical studies, which are not only costly, suffer from a shortage of participants, but also unable to explore all prospective therapeutic solutions. Within the evolving therapeutic landscape, the combined use of radiotherapy (RT) and checkpoint inhibitors (ICIs) emerged as a promising avenue. Exploiting the power of quantitative system pharmacology (QSP), we undertook a study to anticipate the therapeutic outcomes of these interventions, aiming to address the limitations of clinical trials. After enhancing a pre-existing QSP platform and accurately replicating clinical data outcomes, we conducted an in-depth study, examining different treatment protocols with nivolumab and RT, both as monotherapy and in combination, by assessing their efficacy through clinical endpoints, namely time to progression (TTP) and duration of response (DOR). As result, the synergy of combined protocols showcased enhanced TTP and extended DOR, suggesting dual advantages of extended response and slowed disease progression with certain combined regimens. Through the lens of QSP modeling, our findings highlight the potential to fine-tune combination therapies for NSCLC, thereby providing pivotal insights for tailoring patient-centric therapeutic interventions.

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Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

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Quantitative systems pharmacology (QSP) models are an important facet of pharmaceutical and clinical research as they combine mechanistic models of physiology in health and disease with pharmacokinetics/pharmacodynamics to predict systems-level effects. The quantitative clinical pharmacology toolbox has traditionally included both mechanistic modeling and population approaches, collectively called pharmacometrics, but the current landscape requires the optimization and use of multiple models together. Here, we explore several case studies in drug development that exemplify three approaches for using QSP and pharmacometrics models together - parallel synchronization, cross-informative use, and sequential integration. While these approaches are increasingly applied in drug development, achieving a true convergence of QSP and pharmacometrics that fully exploits their synergy will require new tools and methods that enable greater technical integration, in addition to nurturing scientists with diverse modeling expertise that enable cross-discipline strategy. Extensions of existing methods used in each approach as well as additional resources including machine learning models, data-at-scale, end-to-end computation platforms, and real-time analytics will enable this convergence.

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This article introduces the mechanism including antigen presentation, adjuvant, lymphatic system and the characteristics of vaccine, and then summarizes the key applications of core pharmacometrics approaches including QSP, PK/PD, dose response analysis, MBMA, in dose-response, preclinical and clinical translation, and correlation between biomarkers and efficacy of vaccines. It is expected that the successful application of model informed drug development can promote model informed vaccine development so that pharmacometrics makes its due contributions to the development of safer, more effective and more controllable vaccine products.

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Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

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Clinical trials involving brain disorders are notoriously difficult to set up and run. Innovative ways to develop effective prevention and treatment strategies for central nervous system (CNS) diseases are urgently needed. New approaches that are likely to renew or at least modify the paradigms used so far have been recently proposed. Quantitative systems pharmacology (QSP) uses mathematical computerized models to characterize biological systems, disease processes and CNS drug pharmacology. Integrated experimental medicine should increase the probability and predictability of success while controlling clinical trials costs. Finally, the societal perspective and patient empowerment also offer additional approaches to demonstrate the benefit of a new drug in the CNS field.

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Photodynamic therapy (PDT) is a clinically approved therapeutic modality to treat certain types of cancers. However, incomplete ablation of tumor is a challenge. Visible and near IR-activatable prodrug, exhibiting the combined effects of PDT and local chemotherapy, showed better efficacy than PDT alone, without systemic side effects. Site-specifically released chemotherapeutic drugs killed cancer cells surviving from rapid PDT damage via bystander effects. Recently, we developed such a paclitaxel (PTX) prodrug that targets folate receptors. The goals of this study were to determine the optimal treatment conditions, based on modeling, for maximum antitumor efficacy in terms of drug-light interval (DLI), and to investigate the impact of rapid PDT effects on the pharmacokinetic (PK) profiles of the released PTX. PK profiles of the prodrug were determined in key organs and a quantitative systems pharmacology (QSP) model was established to simulate PK profiles of the prodrug and the released PTX. Three illumination time points (DLI = 0.5, 9, or 48 h) were selected for the treatment based on the plasma/tumor ratio of the prodrug to achieve V-PDT (vascular targeted-PDT, 0.5 h), C-PDT (cellular targeted-PDT, 48 h), or both V- and C-PDT (9 h). The anti-tumor efficacy of the PTX prodrug was greatly influenced by the DLI. The 9 h DLI group, when both tumor and plasma concentrations of the prodrug were sufficient, showed the best antitumor effect. The clearance of the released PTX from tumor seemed to be largely impacted by blood circulation. Here, QSP modeling was an invaluable tool for rational optimization of the treatment conditions and for a deeper mechanistic understanding of the positive physiological effect of the combination therapy.

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In this article, we review the past applications of in vitro models in identifying human hepatotoxins and then focus on the use of multiscale experimental models in drug development, including the use of zebrafish and human cell-based, 3-dimensional, microfluidic systems of liver functions as key components in applying Quantitative Systems Pharmacology (QSP). We have implemented QSP as a platform to improve the rate of success in the process of drug discovery and development of therapeutics.

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