Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution

Chakrabarti, Shaon; Michor, Franziska

doi:10.1158/0008-5472.can-16-2871

Cited by 66 publications

(70 citation statements)

References 44 publications

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“…In light of these data, there is significant need for studies into dose reduction and/or altered scheduling to reduce the extremely high (85% (11)) incidence of grade 1 and 2 adverse events. Moreover, further dose and schedule optimisation will facilitate the combination of osimertinib with other drugs while mitigating cumulative toxicities and forestalling resistance (17). To this end, we have investigated the in vitro and in vivo metabolism of osimertinib using recombinant cytochrome P450s, human and murine microsomal preparations, and knockout and humanized mouse lines.…”

Section: Introductionmentioning

confidence: 99%

Identification of Novel Pathways of Osimertinib Disposition and Potential Implications for the Outcome of Lung Cancer Therapy

MacLeod

Lin

Huang

et al. 2018

Clinical Cancer Research

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2 Abstract PurposeOsimertinib is a third-generation inhibitor of the epidermal growth factor receptor used in treatment of non-small cell lung cancer. A full understanding of its disposition and capacity for interaction with other medications will facilitate its effective use as a single agent and in combination therapy. Experimental designRecombinant cytochrome P450s and liver microsomal preparations were used to identify novel pathways of osimertinib metabolism in vitro. A panel of knockout and mouse lines humanized for pathways of drug metabolism were used to establish the relevance of these pathways in vivo. ResultsAlthough some osimertinib metabolites were similar in mouse and human liver samples there were several significant differences, in particular a marked species difference in the P450s involved. The murine Cyp2d gene cluster played a predominant role in mouse, whereas CYP3A4 was the major human enzyme responsible for osimertinib metabolism. Induction of this enzyme in CYP3A4 humanized mice substantially decreased circulating osimertinib exposure. Importantly, we discovered a further novel pathway of osimertinib disposition involving CPY1A1. Modulation of CYP1A1/CYP1A2 levels markedly reduced parent drug concentrations, significantly altering metabolite pharmacokinetics (PK) in humanized mice in vivo. ConclusionWe demonstrate that a P450 enzyme expressed in smokers' lungs and lung tumors has the capacity to metabolise osimertinib. This could be a significant factor in defining the outcome of osimertinib treatment. This work also illustrates how P450-humanized mice can be used to identify and mitigate Acquired resistance to first and second-generation EGFR inhibitors usually involves a further EGFR mutation, T790M. Osimertinib (AZD9291) is the most effective treatment in these latter cases and, due to its low toxicity and high level of brain penetration, is currently being explored as a first-line therapy for metastatic disease. Osimertinib is a cytochrome P450 substrate. These enzymes can define both systemic exposure and intra-tumoral drug concentrations and can therefore be major determinants in the outcome of cancer therapy. We report a novel pathway of osimertinib disposition which may be of importance in this regard.Research.on May 9, 2018.

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Section: Introductionmentioning

confidence: 99%

Identification of Novel Pathways of Osimertinib Disposition and Potential Implications for the Outcome of Lung Cancer Therapy

MacLeod

Lin

Huang

et al. 2018

Clinical Cancer Research

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“…Mathematical modeling studies in particular have been used to explore both broad and detailed aspects of cancer drug resistance, as reviewed in [43,7,25]. The fundamental question of how the presence of drug resistant cells influences tumor dynamics and treatment outcomes has been thoroughly explored in mathematical models under a wide variety of assumptions [14,37,39,47,58,40,23,24,70,16,45,60,63,4,22,33,44,52,65,3,32,80,13,26,29,46,49,51,57,59,77,18,68,1,9,10,20]. Cancer models have also been utilized to assess how various underlying mechanisms contribute to the resistant phenotype [80,13,59,18,20], and to calculate the probability that drug resistance emerges within a specified time frame, be it before or during cancer treatment …”

Section: Introductionmentioning

confidence: 99%

“…The question of how frequently, and at what dose, a single cancer drug should be administered given the presence of (or risk of developing) resistant cells has also been explored through mathematical modeling [37,23,24,33,46,49,68,1,9]. When multiple drugs are available, mathematical models have been used to determine the drug schedule (number of drugs to use, dose, sequence, timing) that best controls tumor progression in spite of drug resistance [39,47,70,16,60,63,4,51,10].Resistance to cancer drugs can be classified as either pre-existing or acquired [34]. Preexisting (intrinsic) drug resistance describes the case in which a tumor contains a subpopulation of drug resistant cells at the initiation of treatment, making the therapy (eventually) ineffective due to resistant cell selection [34].…”

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confidence: 99%

“…As examples, pre-existing BCR-ABL kinase domain mutations confer resistance to the tyrosine kinase inhibitor imatinib in chronic myeloid leukemia patients [66,36], and pre-existing MEK1 mutations confer resistance to BRAF inhibitors in melanoma patients [8]. Many mathematical models have considered how the presence of preexisting resistant cells impact cancer progression and treatment [37,39,58,40,23,24,70,16,60,63,4,22,44,3,32,80,29,49,57,59,77,68,9,10].On the other hand, acquired drug resistance broadly describes the case in which drug resistance develops during the course of therapy from a population of cells that were initially drug sensitive [34]. The term "acquired resistance" is really an umbrella term for two distinct phenomena, which complicates the study of acquired resistance.…”

mentioning

confidence: 99%

“…On the one hand there is resistance that is randomly acquired during the course of treatment, be it due to random genetic mutations or stochastic non-genetic phenotype switching [65]. This spontaneous form of acquired resistance has been considered in many mathematical models [14,39,47,58,40,23,24,45,4,22,33,44,52,32,80,26,46,51,57,10]. On the other hand, drug resistance can be induced (caused) by the drug itself [62,61,69,65,64].…”

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A mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment

Greene

Gevertz

Sontag

2017

Preprint

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Resistance to chemotherapy is a major impediment to the successful treatment of cancer. Classically, resistance has been thought to arise primarily through random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that the progression to drug resistance need not occur randomly, but instead may be induced by the therapeutic agent itself. This process of resistance induction can be a result of genetic changes, or can occur through epigenetic alterations that cause otherwise drug-sensitive cancer cells to undergo phenotype switching. This relatively novel notion of resistance further complicates the already challenging task of designing treatment protocols that minimize the risk of developing drug resistance. In an effort to better understand resistance to treatment, we have developed a mathematical modeling framework that incorporates both random and drug-induced resistance. Our model demonstrates that the ability (or lack thereof) of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. The importance of induced resistance in treatment response led us to ask if, in our model, one can determine the resistance induction rate of a drug for a given treatment protocol. Not only could we prove that the induction parameter in our model is theoretically identifiable, we have also proposed a possible in vitro protocol which could potentially be used to determine a treatment's propensity to induce resistance. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/235150 doi: bioRxiv preprint first posted online Dec. 15, 2017; Tumor resistance to chemotherapy and targeted drugs is a major cause of treatment failure. Both molecular and microenvironmental factors have been implicated in the development of drug resistance [34]. As an example of molecular resistance, the upregulation of drug efflux transporters can prevent sufficiently high intracellular drug accumulation, limiting treatment efficacy [31]. Other molecular causes of drug resistance include modification of drug targets, enhanced DNA damage repair mechanisms, dysregulation of apoptotic pathways, and the presence of cancer stem cells [31,17,34,78,81]. The irregular tumor vasculature which results in inconsistent drug distribution and hypoxia is an example of a microenvironmental factor that impacts drug resistance [76]. Other characteristics of the tumor microenvironment that influence drug resistance include regions of acidity, immune cell infiltration and activation, and the tumor stroma [27,76,56,15,34,55].Research continues to shed light on the multitude of factors that contribute to cancer drug resistance. Mathematical modeling studies in particular have been used to explore both broad and detailed aspects of cancer drug resistance, as reviewed in [43,7,25]. The fundamental ques...

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Dosage strategies for delaying resistance emergence in heterogeneous tumors

Vakil

Trappe

2021

FEBS Open Bio

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Drug resistance in cancer treatments is a frequent problem that, when it arises, leads to failure in therapeutic efforts. Tumor heterogeneity is the primary reason for resistance emergence and a precise treatment design that takes heterogeneity into account is required to postpone the rise of resistant subpopulations in the tumor environment. In this paper, we present a mathematical framework involving clonal evolution modeling of drug-sensitive and drug-resistant clones. Using our framework, we examine delaying the rise of resistance in heterogeneous tumors during control phase of therapy in a containment treatment approach. We apply pharmacokinetic/pharmacodynamic (PKPD) modeling and show that dosage strategies can be designed to control the resistant subpopulation. Our results show that the drug dosage and schedule determine the relative dynamics of sensitive and resistant clones. We present an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence for a given period of containment treatment.

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Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution

Cited by 66 publications

References 44 publications

Identification of Novel Pathways of Osimertinib Disposition and Potential Implications for the Outcome of Lung Cancer Therapy

Identification of Novel Pathways of Osimertinib Disposition and Potential Implications for the Outcome of Lung Cancer Therapy

A mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment

Dosage strategies for delaying resistance emergence in heterogeneous tumors

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