CPT: Pharmacometrics & Systems Pharmacology | 2021
Mechanistic models for hematological toxicities: Small is beautiful
Abstract
There is a rising trend to implement algorithmbased tools for precision oncology. Several strategies are available, ranging from complex approaches to simpler phenomenological models. Whereas complex models can help understanding the underlying cellular or molecular mechanisms, phenomenological models are merely descriptive. Conversely, such simple models are the most likely to reach bedside application because of their simplicity. It is critical to balance the pros and cons of each strategy for precision medicine in realworld settings. Developing mathematical models to describe pharmacodynamic end points with anticancer drugs is a rising strategy in precision medicine in oncology. For instance, an analysis aiming at characterizing resonance (i.e., neutrophil oscillations) in young patients treated by cyclic chemotherapy has been recently presented.1 Complex quantitative systems pharmacology (QSP) modeling applied to granulopoïesis was used to demonstrate that timing of chemotherapy could impact the dynamics of neutrophils. This kind of work suggests that modelinformed scheduling could help limiting hematological toxicity, for example, by delaying supportive granulocyte colonystimulating factor (GCSF) therapy, thus eventually improving clinical outcomes at the bedside. The issue of controlling druginduced adverse events, especially hematological toxicities with cytotoxics, is critical in many respects. Pancytopenia can be rapidly lifethreatening, especially in frail patients. When they do not directly lead to toxic death, such severe toxicities frequently oblige practitioners to postpone or discontinue chemotherapy or associated radiation therapy, thus eventually affecting clinical outcomes and survival. Altogether, developing strategies to control or reduce the risk of druginduced hematological toxicities is therefore a major concern in clinical oncology, especially because cytotoxics are still today the backbone of most treatments of solid tumors and hematological malignancies. Developing in silico approaches as decisionmaking tools to optimize anticancer therapies has been a rising trend for decades in clinical oncology.2 Pharmacokinetically guided regimens with Bayesian adaptive dosing procedures have been already proposed for several years to tailor the administration of a variety of cytotoxics and oraltargeted therapies.3 However, implementing adaptive dosing strategies in routine clinical settings remains challenging. Realworld precision medicine requires mathematical models that are kept simple enough to allow proper identification of their parameters. This is a prerequisite for being easily applied prospectively in actual patients and not to be used solely as part of retrospective in silico modeling. This calls for using primarily topdown approaches, such as compartmental analysis, before developing pharmacokinetics/pharmacodynamics (PK/PD) models likely to help oncologists determine the optimal dosing and scheduling of a given drug to a given patient. More intricate modeling and QSP approaches are appealing strategies that are unfortunately impaired by their intrinsic complexity, which has made them unfit for routine use at the bedside so far (Figure 1). The complexity of a model should fit to the data made actually available to infer unknown parameters, thus ensuring to make meaningful predictions. When the dimensionality of a model is too large, such predictions are practically intractable because they are associated with large uncertainty. Nevertheless, QSP models might be more appropriate to specific problems, especially if they involve measurable physiological parameters that can be extrapolated, for example, from animals to humans such as for determining the firstinhuman dose of immune checkpoint inhibitors such as antiPD1 pembrolizumab.4 In addition, they offer mechanistic insights that help develop a quantitative understanding of complex pharmacopathophysiological processes. Conversely, phenomenological modeling could in many respects look like an oversimplistic, suboptimal strategy, often mocked as being “black boxes” simply linking an output to a given input. However, such models have demonstrated their utility in realworld settings, not despite the fact that they are black boxes, but precisely because they are black boxes.5 For instance, the Friberg model is a simplified representation of hematopoiesis using a semimechanistic, compartmental description of the proliferation and dynamics of the maturation of blood progenitors.3 Because of its smart