Tumor dynamics in patients with solid tumors treated with pembrolizumab beyond disease progression.

While many patients are treated beyond progression (TBP), the magnitude and duration of clinical benefit in these patients have not been fully quantified. Data from 799 patients with melanoma (n = 176), non-small cell lung cancer (NSCLC; n = 146), gastric cancer (GC; n = 87), head and neck squamous cell carcinoma (HNSCC; n = 112), clear-cell renal cell carcinoma (ccRCC; n = 51), and urothelial carcinoma (UC; n = 227) TBP were included. Patients had received pembrolizumab beyond confirmed progressive disease (PD) per RECIST v1.1. A subset of patients displays a 30% reduction in the sum of lesion diameters in the post-progression period (melanoma 24.4%, NSCLC 11.6%, 12.6% GC, 8.9% HNSCC, 15.7% ccRCC, and 13.2% UC). Most patients show stable target lesion dynamics in the post-progression period (melanoma, 64.8%; NSCLC, 72.6%; GC, 69.0%, 75.9% HNSCC, 72.5% ccRCC, 75.3% UC). Pembrolizumab generates meaningful efficacy in a subset of patients treated beyond RECIST v1.1 progression.

Incorporating lesion-to-lesion heterogeneity into early oncology decision making.

RECISTv1.1 (Response Evaluation Criteria In Solid Tumors) is the most commonly used response grading criteria in early oncology trials. In this perspective, we argue that RECISTv1.1 is ambiguous regarding lesion-to-lesion variation that can introduce bias in decision making. We show theoretical examples of how lesion-to-lesion variability causes bias in RECISTv1.1, leading to misclassification of patient response. Next, we review immune checkpoint inhibitor (ICI) clinical trial data and find that lesion-to-lesion heterogeneity is widespread in ICI-treated patients. We illustrate the implications of ignoring lesion-to-lesion heterogeneity in interpreting biomarker data, selecting treatments for patients with progressive disease, and go/no-go decisions in drug development. Further, we propose that Quantitative Systems Pharmacology (QSP) models can aid in developing better metrics of patient response and treatment efficacy by capturing patient responses robustly by considering lesion-to-lesion heterogeneity. Overall, we believe patient response evaluation with an appreciation of lesion-to-lesion heterogeneity can potentially improve decision-making at the early stage of oncology drug development and benefit patient care.

RECISTv1.1 progression in oncology: Shades of gray.

The Response Evaluation Criteria in Solid Tumors (RECIST)-based outcomes, such as objective response rate (ORR) or progression free survival (PFS), are standard outcomes for early oncology trials. These indices provide a black-and-white interpretation of response to therapy. We propose that lesion-level analysis and mechanism-based pharmacodynamic endpoints may provide a more informative index of response to therapy. Accounting for "shades of gray" in lesion-level response assessments may reduce bias in go/no-go decisions and biomarker analyses for novel oncology compounds and discontinuation decisions for individual patients.

Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors.

Immune checkpoint inhibitors, administered as single agents, have demonstrated clinical efficacy. However, when treating cold tumors, different combination strategies are needed. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of immunotherapy combinations in cold tumors. Tumor size of mice treated with TC-1/A9 non-inflamed tumors and the drug effects of an antigen, a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (anti-programmed cell death 1 antibody) were modeled using Monolix and following a middle-out strategy. Tumor growth was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm3 and a doubling time of 3.6 days. In the treatment groups, contrary to the lack of response observed in monotherapy, combinations including the antigen were able to induce an antitumor response. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that CD8+ T lymphocytes and resistance mechanisms are strongly related to the clinical outcome. The activation of antigen-presenting cells might be needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. These models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.

Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response.

A quantitative systems pharmacology model for metastatic melanoma was developed for immuno-oncology with the goal of predicting efficacy of combination checkpoint therapy with pembrolizumab and ipilimumab. This literature-based model is developed at multiple scales: (i) tumor and immune cell interactions at a lesion level; (ii) multiple heterogeneous target lesions, nontarget lesion growth, and appearance of new metastatic lesion at a patient level; and (iii) interpatient differences at a population level. The model was calibrated to pembrolizumab and ipilimumab monotherapy in patients with melanoma from Robert et al., specifically, waterfall plot showing target lesion response and overall response rate (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1), which additionally considers nontarget lesion growth and appearance of new metastatic lesions. We then used the model to predict waterfall and RECIST version 1.1 for combination treatment reported in Long et al. A key insight from this work was that nontarget lesions growth and appearance of new metastatic lesion contributed significantly to disease progression, despite reduction in target lesions. Further, the lesion level simulations of combination therapy show substantial efficacy in warm lesions (intermediary immunogenicity) but limited advantage of combination in both cold and hot lesions (low and high immunogenicity). Because many patients with metastatic disease are expected to have a mixture of these lesions, disease progression in such patients may be driven by a subset of cold lesions that are unresponsive to checkpoint inhibitors. These patients may benefit more from the combinations which include therapies to target cold lesions than double checkpoint inhibitors.

Modulation of intratumoural myeloid cells, the hallmark of the anti-tumour efficacy induced by a triple combination: tumour-associated peptide, TLR-3 ligand and α-PD-1.

BACKGROUND: Anti-programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1) monoclonal antibodies (mAbs) show remarkable clinical anti-tumour efficacy. However, rational combinations are needed to extend the clinical benefit to primary resistant tumours. The design of such combinations requires the identification of the kinetics of critical immune cell populations in the tumour microenvironment. METHODS: In this study, we compared the kinetics of immune cells in the tumour microenvironment upon treatment with immunotherapy combinations with different anti-tumour efficacies in the non-inflamed tumour model TC-1/A9. Tumour-bearing C57BL/6J mice were treated with all possible combinations of a human papillomavirus (HPV) E7 long peptide, polyinosinic-polycytidylic acid (PIC) and anti-PD-1 mAb. Tumour growth and kinetics of the relevant immune cell populations were assessed over time. The involvement of key immune cells was confirmed by depletion with mAbs and immunophenotyping with multiparametric flow cytometry. RESULTS: The maximum anti-tumour efficacy was achieved after intratumoural administration of HPV E7 long peptide and PIC combined with the systemic administration of anti-PD-1 mAb. The intratumoural immune cell kinetics of this combination was characterised by a biphasic immune response. An initial upsurge of proinflammatory myeloid cells led to a further rise in effector CD8+ T lymphocytes at day 8. Depletion of either myeloid cells or CD8+ T lymphocytes diminished the anti-tumour efficacy of the combination. CONCLUSIONS: The anti-tumour efficacy of a successful immunotherapy combination in a non-inflamed tumour model relies on an early inflammatory process that remodels the myeloid cell compartment.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape.

A cross-industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Quantification of the relationship between glycemia and beta-cell mass adaptation in vivo.

Beta-cell mass dynamics play an important role in the adaptation to obesity, as well as in the pathogenesis of type 2 diabetes. Here we used a 24-hour modified hyperglycemic clamp protocol to investigate the effect of increasing glucose concentrations (15, 20, 25, or 35 mmol/L) on beta-cell mass and rates of beta-cell replication, death, and neogenesis in 6-week-old Sprague Dawley rats (n = 40). During the first 4 h of glucose infusion, plasma insulin levels rose to an approximate steady state in each group, but by the end of 24 h, there was no difference in insulin levels between any of the groups. There was also no difference in beta-cell mass between groups. Mean beta-cell replication rates displayed a linear relationship to mean plasma glucose levels in all hyperglycemic animals (r(2) = 0.98, p < 0.05). Relative to the uninfused basal control animals, replication rates were significantly reduced in the 15 mmol/L glucose group. The percentage of TUNEL-positive beta-cells was not different between groups. There was also no significant difference in markers of neogenesis. Thus, these data demonstrate that hyperglycemia for 24 h had no effect on beta-cell mass, death, or neogenesis in 6-week-old Sprague Dawley rats. We demonstrate a linear relationship, however, between hyperglycemia and beta-cell replication rates in vivo.

Treatment with sitagliptin or metformin does not increase body weight despite predicted reductions in urinary glucose excretion.

BACKGROUND: We used a mathematical model to estimate the contribution of urinary glucose excretion (UGE) to reported changes in body weight (BW) following oral antihyperglycemic agent (AHA) therapy. This modeling approach was used to gain novel insight into the mechanisms by which oral AHA affects BW. METHODS: Twenty-four hour glucose profiles were used to predict UGE before and after treatment with oral AHA. Model-predicted changes in BW due to reduced UGE were compared with reported changes in BW to quantify non-UGE-dependent effects (fluid retention, food intake, and energy expenditure). RESULTS: In type 2 diabetes patients [hemoglobin A1c (HbA1c) >7.3%], the energy lost to UGE is predicted to decrease an average of 100 kcal/day for each 1% decrease in HbA1c. This effect, alone, is predicted to increase BW 1.4 kg after 6 months. Differences from this value reported for changes in BW with oral AHA therapy (+1.4 kg for pioglitazone and rosiglitazone; -0.4 kg for glyburide; -0.9 kg for sitagliptin and vildagliptin; -2.3 kg for metformin) are therefore predicted to be due to additional, non-UGE-dependent mechanisms. CONCLUSIONS: Weight gain following thiazolidinedione therapy is predicted to result from both reduced UGE and non-UGE-dependent mechanisms. Reduced UGE alone is predicted to account for most of the weight gain reported following sulfonylurea therapy. Weight loss observed in response to metformin and weight maintenance observed in response to dipeptidyl peptidase-4 inhibitors may result from an increase in satiety, energy expenditure, or both.

Dynamics of insulin sensitivity, -cell function, and -cell mass during the development of diabetes in fa/fa rats.

Both male Zucker Fatty (mZF) and lower-fat-fed female Zucker diabetic fatty (LF-fZDF) rats are obese but remain normoglycemic. Male ZDF (mZDF) and high-fat-fed female ZDF rats (HF-fZDF) are also obese but develop diabetes between 7 and 10 wk of age. Although these models have been well studied, the mechanisms governing the adaptations to obesity in the normoglycemic animals, and the failure of adaptation in the animals that develop diabetes, remain unclear. Here we use quantitative morphometry and our recently developed coupled beta-cell mass (beta(m)), insulin, and glucose model to elucidate the dynamics of insulin sensitivity (S(I)), beta-cell secretory capacity (beta(sc)), and beta(m) in these four animal models. Both groups that remained normoglycemic with increasing obesity (mZF, LF-fZDF) exhibited increased beta(m) and constant beta(sc) in response to a falling S(I). In rats that developed hyperglycemia (mZDF, HF-fZDF), there was a greater reduction in S(I) and slower expansion of beta(m), with constant beta(sc). beta(sc) decreased after glucose levels rose above 20 mM. Taken together, these data suggest that excessive insulin resistance and insufficient beta(m) adaptation play a primary role in the pathogenesis of diabetes.