Is immune checkpoint modulation a potential therapeutic option in triple negative breast cancer?

Atezolizumab Plus Chemotherapy vs. Chemotherapy in Advanced or Metastatic Triple-Negative Breast Cancer: A Cost-Effectiveness Analysis

Purpose: The IMpassion130 trial demonstrated the efficacy of adding atezolizumab to paclitaxel for advanced or metastatic triple-negative breast cancer (TNBC). The current study evaluated the cost-effectiveness of adding atezolizumab to nab-paclitaxel for TNBC from the perspective of Chinese health sector.

Methods: A partitioned survival model was implemented for patients with TNBC. The survival data were derived from IMpassion130 trial. Direct costs and utility values were collected from the Chinese Drug Bidding Database and published literatures. The primary analysis outcomes were quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). Sensitivity analyses were performed to observe model stability.

Results: In the base-case analysis, the ICER of atezolizumab plus nab-paclitaxel vs. nab-paclitaxel is respectively, $176,056/QALY, $118,146/QALY, and $323,077/QALY in the ITT, PD-L1(+) and PD-L1(–) group.

Conclusion: Adding atezolizumab to nab-paclitaxel could improve survival time significantly in the PD-L1-positive group, but it is not a cost-effective strategy compared to nab-paclitaxel monotherapy for Chinese patients with advanced or metastatic triple-negative breast cancer in the current economic context of China.

Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study

Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a "one-gene, one-receptor, one-mechanism" paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.