An Integrated Disease/Pharmacokinetic/Pharmacodynamic Model Suggests Improved Interleukin-21 Regimens Validated Prospectively for Mouse Solid Cancers

Interleukin (IL)-21 is an attractive antitumor agent with potent immunomodulatory functions. Yet thus far, the cytokine has yielded only partial responses in solid cancer patients, and conditions for beneficial IL-21 immunotherapy remain elusive. The current work aims to identify clinically-relevant IL-21 regimens with enhanced efficacy, based on mathematical modeling of long-term antitumor responses. For this purpose, pharmacokinetic (PK) and pharmacodynamic (PD) data were acquired from a preclinical study applying systemic IL-21 therapy in murine solid cancers. We developed an integrated disease/PK/PD model for the IL-21 anticancer response, and calibrated it using selected “training” data. The accuracy of the model was verified retrospectively under diverse IL-21 treatment settings, by comparing its predictions to independent “validation” data in melanoma and renal cell carcinoma-challenged mice (R2>0.90). Simulations of the verified model surfaced important therapeutic insights: (1) Fractionating the standard daily regimen (50 µg/dose) into a twice daily schedule (25 µg/dose) is advantageous, yielding a significantly lower tumor mass (45% decrease); (2) A low-dose (12 µg/day) regimen exerts a response similar to that obtained under the 50 µg/day treatment, suggestive of an equally efficacious dose with potentially reduced toxicity. Subsequent experiments in melanoma-bearing mice corroborated both of these predictions with high precision (R2>0.89), thus validating the model also prospectively in vivo. Thus, the confirmed PK/PD model rationalizes IL-21 therapy, and pinpoints improved clinically-feasible treatment schedules. Our analysis demonstrates the value of employing mathematical modeling and in silico-guided design of solid tumor immunotherapy in the clinic

Pharmacodynamic therapeutic drug monitoring for cancer: challenges, advances, and future opportunities

In the modern era of cancer treatment, with targeted agents superseding more traditional cytotoxic chemotherapeutics, it is becoming increasingly important to use stratified medicine approaches to ensure that patients receive the most appropriate drugs and treatment schedules. In this context, there is significant potential for the use of pharmacodynamic biomarkers to provide pharmacological information, which could be used in a therapeutic drug monitoring setting. This review focuses on discussing some of the challenges faced to date in translating preclinical pharmacodynamic biomarker approaches to a clinical setting. Recent advances in important areas including circulating biomarkers and pharmacokinetic/pharmacodynamic modeling approaches are discussed, and selected examples of anticancer drugs where there is existing evidence to potentially advance pharmacodynamic therapeutic drug monitoring approaches to deliver more effective treatment are discussed. Although we may not yet be in a position to systematically implement therapeutic drug monitoring approaches based on pharmacodynamic information in a cancer patient setting, such approaches are likely to become more commonplace in the coming years. Based on ever-increasing levels of pharmacodynamic information being generated on newer anticancer drugs, facilitated by increasingly advanced and accessible experimental approaches available to researchers to collect these data, we can now look forward optimistically to significant advances being made in this area

Modelling The Cancer Growth Process By Stochastic Delay Diffferential Equations Under Verhults And Gompertz's Law

In this paper, the uncontrolled environmental factors are perturbed into the intrinsic growth rate factor of deterministic equations of the growth process. The growth process under two different laws which are Verhults and Gompertz’s law are considered, thus leading to stochastic delay differential equations (SDDEs) of logistic and Gompertzian, respectively. Gompertzian deterministic model has been proved to fit well the clinical data of cancerous growth, however the performance of stochastic model towards clinical data is yet to be confirmed. The prediction quality of logistic and Gompertzian SDDEs are evaluating by comparing the simulated results with the clinical data of cervical cancer growth. The parameter estimation of stochastic models is computed by using simulated maximum likelihood method. We adopt 4-stage stochastic Runge-Kutta to simulate the solution of stochastic models

Therapeutic implications of Notch signaling in ovarian cancer

Falta palabras claveThe cancer stem cell (CSC) theory states that only some cells from a heterogeneous tumor are capable of tumorigenesis. These cells have been collectively termed ‘CSCs’ due to their stem cell-like properties of self-renewal, differentiation and tumorigenesis. It is therefore expected that therapies that target the key signalling pathways involved in CSC biology would ultimately lead to improved outcomes for the treatment of cancer patients. Notch is an evolutionary conserved signalling pathway involved in CSCs and in the development and progression of malignant tumors, like ovarian cancer. Gamma-secretase inhibitors (GSIs) are small molecules able to inhibit the Notch signalling pathway and exert antitumor effects in preclinical models. RO4929097 is one the first GSIs that entered clinical development in solid tumors based on promising early clinical efficacy data. As a result, its clinical evaluation has been conducted both in combination with chemotherapy and other targeted agents (e.g. temsirolimus) and as a single-agent in selected malignancies (e.g. ovarian cancer). The results presented herein have demonstrated that RO4929097 has a tolerable safety profile both in combination with temsirolimus in patients with solid tumors and as a single agent in women with advanced ovarian cancer. However, based on its unfavourable pharmacokinetic profile and its limited antitumor efficacy further clinical development will not be pursued. A better understanding of the biology of CSCs and their role in tumor progression, coupled with increased knowledge about the Notch signalling pathway will likely facilitate the development of other Notch-targeted agents aimed at having a clinical meaningful impact in outcomes of cancer patients

Pazopanib in advanced soft tissue sarcomas.

Pazopanib is the first and only tyrosine kinase inhibitor currently approved for the treatment of multiple histological subtypes of soft tissue sarcoma (STS). Initially developed as a small molecule inhibitor of vascular endothelial growth factor receptors, preclinical work indicates that pazopanib exerts an anticancer effect through the inhibition of both angiogenic and oncogenic signaling pathways. Following the establishment of optimal dosing and safety profiles in early phase studies and approval for the treatment of advanced renal cell carcinoma, pazopanib was investigated in STS. A landmark phase III randomized study demonstrated improved progression-free survival with pazopanib compared to that with placebo in pretreated patients with STS of various subtypes. The efficacy of pazopanib in specific STS subtypes has been further described in real-world-based case series in both mixed and subtype-specific STS cohorts. At present, there are no clinically validated predictive biomarkers for use in selecting patients with advanced STS for pazopanib therapy, limiting the clinical effectiveness and cost-effectiveness of the drug. In this review, we summarize the preclinical and clinical data for pazopanib, outline the evidence base for its effect in STS and explore reported studies that have investigated putative biomarkers

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment—with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges