Shaping the AML Treatment Landscape—Modeling a Path through Plenty, Uncertainty, and Paucity

Introduction

The last two decades have identified and characterized heterogeneities arising in the genetic structure of the bone marrow malignancy, acute myeloid leukemia (AML), to partly explain the variation in outcomes among similarly treated patients.[1] In high-income countries, treatment paradigms for AML have now shifted to include conventional chemotherapy and/or small molecule drugs directed against biological targets, deemed disease-defining.[1] [2] [3] Apart from the acute promyelocytic leukemia variant,[4] however, AML remains incurable for a significant number of patients within different disease subgroups. In addition, the incremental survival gain with small molecule drugs is relatively modest,[2] [3] [5] and the costs associated with therapy, supportive care, and disease-monitoring remain considerable. In low-and middle-income countries, financial constraints often render therapies, considered “standard-of-care” in higher income countries, prohibitively expensive.[6] Increasingly, the rarity of biological subtypes of AML[1] and the availability of multiple drugs targeting unique disease sub-types[2] [5] [7] [8] are also beginning to present challenges to the design of contemporaneous clinical trials. To optimize clinical benefits and the cost-effectiveness of therapy to patients and healthcare systems, as well as to address key clinical hypotheses, an innovative approach for hypothesis testing and identifying best therapy is, therefore, required.

In recent years, the pharmaceutical industry and regulators have increasingly turned to modeling and simulation to investigate drug–drug interactions,[9] assess the exposure and toxicological impacts of various compounds,[10] [11] and reduce reliance on animal experiments for identifying new products.[12] In contrast, physicians have depended solely on the statistical output of adequately powered clinical trials to guide treatment decisions. The existence of clinical trial data and associated publicly available genomic datasets, along with increasingly sophisticated mathematical and computational methodologies, presents a significant opportunity to make progress in the challenging arena of AML therapeutics. Here, we highlight three problem areas relevant to the therapy or monitoring of AML that could benefit from an integrated biological and mathematical approach.

Publikationsverlauf

Artikel online veröffentlicht:
30. April 2024

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