Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?

Ciccolini, Joseph; Barbolosi, Dominique; André, Nicolas; Benzekry, Sébastien

doi:10.1200/po.19.00381

Cited by 10 publications

(14 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although a dynamic multiparameter model might outperform the predictive ability of any single feature, incorporating different components relies on the harmonization of diverse assays and multiple selective biomarkers will be crucial. Incorporating non-liquid components like radiomic imaging analysis and tumor features ( 73 ), coupled with multidimensional approaches involving blood-based proteomic testing ( 74 ) and mechanistic learning ( 75 , 76 ), is also required. Additionally, consistent cross-study validation and standardization of each model are required before any implementation in routine clinical use to improve personalized medicine approaches, which could be addressed through ongoing prospective collaborative molecular response-adaptive clinical trials ( Table 1 ).…”

Section: On the Horizon Challenges To Be Overcomementioning

confidence: 99%

Applications of Circulating Tumor DNA in Immune Checkpoint Inhibition: Emerging Roles and Future Perspectives

et al. 2022

View full text Add to dashboard Cite

Immune checkpoint inhibitors (ICIs), especially anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) antibodies, have made dramatic progress in the treatment of lung cancer, especially for patients with cancers not driven by oncogenes. However, responses are limited to a subset of patients, and which subset of patients will optimally benefit from ICI remains unknown. With the advantage of being minimally invasive and dynamic, noninvasive biomarkers are promising candidates to predict response, monitor resistance, and track the evolution of lung cancer during ICI treatment. In this review, we focus on the application of circulating tumor DNA (ctDNA) in plasma in immunotherapy. We examine the potential of pre- and on-treatment features of ctDNA as biomarkers, and following multiparameter analysis, we determine the potential clinical value of integrating predictive liquid biomarkers of ICIs to optimize patient management. We further discuss the role of ctDNA in monitoring treatment resistance, as well as challenges in clinical translation.

show abstract

Section: On the Horizon Challenges To Be Overcomementioning

confidence: 99%

Applications of Circulating Tumor DNA in Immune Checkpoint Inhibition: Emerging Roles and Future Perspectives

et al. 2022

View full text Add to dashboard Cite

show abstract

“…84 Meanwhile, mechanistic modeling could play an important role in determining the best modes of combination between ICI and established treatments (surgery, radiotherapy, chemotherapy, and targeted therapy). 85,86 Figure 2 Overview of mechanistic methods in oncology. Departing from the dose (known), such models accommodate for four main types of quantitative data: drug concentrations, efficacy (e.g., tumor size), safety (e.g., neutrophil counts), and survival data.…”

Section: Modeling In (Immune-)onco-pharmacologymentioning

confidence: 99%

“…More generally, the question of the optimal combination between different anticancer agents with different modes of action is well-adapted for mathematical modeling. 85 For instance, several studies have investigated the combination of anti-angiogenics with chemotherapy. 91,92 The biological rationale is that anti-angiogenic agents, such as bevacizumab, induce a transient amelioration of the tortuous and poorly functional blood vessel network of a tumor, in a process called vascular normalization.…”

Section: Modeling In (Immune-)onco-pharmacologymentioning

confidence: 99%

“…143,144 Another valuable approach is to derive quantitative metrics from simulation outputs of mechanistic modeling and use them as ML inputs for predictive purposes. 145 We propose to name such hybrid approaches combining big data and ML with mechanistic modeling "mechanistic learning" 85 (Figure 4).…”

Section: Perspectives For Combining Ai and Mathematical Modeling: Mecmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

Benzekry

2020

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

The amount of “big” data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: “mechanistic learning.”

show abstract

“…114 As such, immune checkpoint inhibitors would benefit from a better rationale using dedicated PK/PD models when setting up such combinations. 115…”

Section: Beyond Dosing: Finding the Right Scheduling With Combinatorimentioning

confidence: 99%

Towards Rational Cancer Therapeutics: Optimizing Dosing, Delivery, Scheduling, and Combinations

Ferrer

Fanciullino

Milano

et al. 2020

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

The current trend to personalize anticancer therapies mostly relies on selecting the best drug or combination of drugs to achieve optimal efficacy in patients. In addition to the comprehensive genetic and molecular knowledge of each tumor before choosing the drugs to be given, there is probably much room left for improvement by further personalizing the very modes by which the drugs are given, once they have been carefully selected. In particular, shifting from standard dosing to tailored dosing should help in maintaining drug exposure levels in the right therapeutic window, thus ensuring that the efficacy/toxicity balance is optimal. This paper covers the current knowledge regarding pharmacokinetic/pharmacodynamic relationships of anticancer agents, from decades‐old cytotoxics to the latest immune checkpoint inhibitors, the most frequent sources for long‐neglected interpatient variability impacting on drug exposure levels, and what could be done to achieve real personalized medicine in oncology such as implementing therapeutic drug monitoring with adaptive dosing strategies or using model‐driven modalities for personalized dosing and scheduling.

show abstract

Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?

Abstract: Author affiliations and support information (if applicable) appear at the end of this article.

Cited by 10 publications

References 48 publications

Applications of Circulating Tumor DNA in Immune Checkpoint Inhibition: Emerging Roles and Future Perspectives

Applications of Circulating Tumor DNA in Immune Checkpoint Inhibition: Emerging Roles and Future Perspectives

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

Towards Rational Cancer Therapeutics: Optimizing Dosing, Delivery, Scheduling, and Combinations

Contact Info

Product

Resources

About