Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt’s lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.Koch Institute for Integrative Cancer Research (Support (core) Grant P30-CA14051)National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Institute of General Medical Sciences (U.S.) (Interdepartmental Biotechnology Training Program 5T32GM008334

Stochastic Norton-Simon-Massagu\ue9 Tumor Growth Modeling: Controlled and Mixed-Effects Uncontrolled Analysis

Tumorigenesis is a complex process that is heterogeneous and affected by numerous sources of variability. This study presents a stochastic extension of a biologically grounded tumor growth model, referred to as the Norton-Simon-Massagu\ue9 (NSM) tumor growth model. We first study the uncontrolled version of the model where the effect of chemotherapeutic drug agent is absent. Conditions on the model\u2019s parameters are derived to guarantee the positivity of the tumor volume and hence the validity of the proposed stochastic NSM model. To calibrate the proposed model we utilize a maximum likelihood- based estimation algorithm and population mixed-effect modeling formulation. The algorithm is tested by fitting previously published tumor volume mice data. Then, we study the controlled version of the model which includes the effect of chemotherapy treatment. Analysis of the influence of adding the control drug agent into the model and how sensitive it is to the stochastic parameters is performed both in open-loop and closed-loop viewpoints through different numerical simulations

Modeling Tumor Clonal Evolution for Drug Combinations Design

Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure toward drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review we discuss the promising opportunities that these interdisciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.David H. Koch Cancer Research Fund (Grant P30-CA14051)National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Institute of General Medical Sciences (U.S.). Interdepartmental Biotechnology Training Program (5T32GM008334

Anti-angiogenic drug scheduling optimisation with application to colorectal cancer

Bevacizumab (bvz) is a first choice anti-angiogenic drug in oncology and is primarily administered in combination with chemotherapy. It has been hypothesized that anti-angiogenic drugs enhance efficacy of cytotoxic drugs by “normalizing” abnormal tumor vessels and improving drug penetration. Nevertheless, the clinical relevance of this phenomenon is still unclear with several studies over recent years suggesting an opposing relationship. Herein, we sought to develop a new computational tool to interrogate anti-angiogenic drug scheduling with particular application in the setting of colorectal cancer (CRC). Specifically, we have employed a mathematical model of vascular tumour growth which interrogates the impact of anti-angiogenic treatment and chemotherapeutic treatment on tumour volume. Model predictions were validated using CRC xenografts which underwent treatment with a clinically relevant combinatorial anti-angiogenic regimen. Bayesian model selection revealed the most appropriate term for capturing the effect of treatments on the tumour size, and provided insights into a switch-like dependence of FOLFOX delivery on the tumour vasculature. Our experimental data and mathematical model suggest that delivering chemotherapy prior to bvz may be optimal in the colorectal cancer setting

Validação de um sistema de classificação de pacientes para a prestação de cuidados de enfermagem em ambulatório de oncologia

O presente estudo engloba conceitos e contextualizações de um modelo assistencial para a equipe de enfermagem em um ambulatório de quimioterapia validando um número suficiente de enfermeiros para um atendimento de excelência aos pacientes em tratamento para o câncer. Um dos maiores desafios em um ambulatório de quimioterapia é encontrar o número ideal de enfermeiros que sejam capacitados e atendam com qualidade o paciente oncológico. Treinamento e capacitação contínua são os pontos chave para uma equipe qualificada e a altura de propiciar uma assistência completa. A metodologia de classificação de criticidade do paciente oncológico foi estudada nesta dissertação por oferecer mais enfoque aos processos associados à prestação de cuidados profissionais de enfermagem, levando em consideração a performance status do paciente oncológico em regime de tratamento ambulatorial. A utilização de níveis de criticidade auxilia o planejamento da assistência, sendo o maior nível aplicado aos pacientes como uma representação de maior atenção a ser dada pelo enfermeiro para seu paciente. Como conclusão, a metodologia que utiliza a criticidade do paciente caracterizada por níveis, mostrou que a utilização de 21 pontos diários é a ideal para obter-se o numero adequado de enfermeiros em um ambulatório de quimioterapia.The present study encompasses concepts and contextualizations of a care model for the nursing staff in a chemotherapy outpatient clinic validating a sufficient number of nurses to provide excellent care to patients undergoing cancer treatment. One of the biggest challenges in a chemotherapy outpatient clinic is to find the ideal number of nurses who are qualified and provide quality care to the cancer patient. Ongoing training and capacity building are the key points for a qualified team and the time to provide complete assistance. The methodology of criticality classification of cancer patients was studied because it offers more focus on the processes associated with the provision of professional nursing care, taking into consideration the performance status of cancer patients on an outpatient basis. The use of criticality levels helps care planning, with the highest level applied to patients as a representation of greater attention to be given by nurses to their patients. In conclusion, the methodology that uses the criticality of the patient characterized by levels, showed that the use of 21 daily points is ideal to obtain the adequate number of nurses in a chemotherapy outpatient clinic

Stochastic Models for Improving Screening and Surveillance Decisions for Prostate Cancer Care

Recent advances in the development of new technologies for the early detection and treatment of cancer have the potential to improve patient survival and lower the cost of treatment by catching cancer at an early stage. However, there is little research investigating the health and economic implications of these new technologies. For example, magnetic resonance imaging (MRI) and new biomarker tests have been proposed as potential minimally invasive ways to achieve early detection of prostate cancer. These new technologies vary in their sensitivity and specificity leading to both false-positive and false-negative results that can have serious health implications for patients. Moreover, due to the high cost and imperfect nature of these new tests, whether and when to use these tests is unclear. We present stochastic models for prostate cancer disease onset and progression that incorporates partial observability of a patient's prostate cancer health status. We used statistical learning algorithms and clinical datasets combined with expert clinical knowledge of urologists at the University of Michigan to estimate and validate the models. The models can simulate progression through prostate cancer states to mortality from prostate cancer or other causes for a population of patients. New technologies, such as MRI and biomarker tests, are incorporated into the model using a probabilistic representation of test outcomes to represent the information these tests provide about the true health status of the patient. Since these technologies can be used in varying ways, the choice of tests and optimal times to initiate tests are treated as decision variables in the model. We calibrated and validated our models using several data sources and subsequently used our models to design optimal testing strategies that trade-off the harms and benefits of using these new technologies. Our results show that these new technologies can lead to significantly improved health outcomes and they are cost-effective relative to established norms for societal willingness-to-pay. We have also used these models to provide important insights about the optimal timing of prostate biopsies for men with low-risk prostate cancer undergoing active surveillance. By using new technologies to better select men for biopsy and by improving active surveillance strategies, physicians can reduce the harms of prostate cancer screening (e.g., unnecessary biopsies and overtreatment of low-risk disease) while continuing to reduce prostate cancer deaths through screening and early detection. The methodological approaches we present in this thesis could be applied to many other chronic diseases, including bladder, breast, and colorectal cancer.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136969/1/clbarnet_1.pd

Use of Markov Decision Process Models in Preventive Medicine

The biggest trade-off when proposing health care policies is about balancing the effectiveness and the practicality of the policies. The optimal policies providing benchmark performances can be driven through using operations research tools; however, they usually have complex structures that are necessary to sufficiently represent various aspects of the system being modeled. There are also policies either proposed in guidelines or followed in practice but they often vary with the system characteristics, i.e., preferences of the clinicians, available resources of the clinics, etc. Therefore, standardized, simple yet effective policies are needed for many healthcare applications, including preventive medicine. At this point, we study developing health care delivery policies that maximize the effect of the preventive interventions, while providing applicable policy structures that can be easily followed by health practitioners in practice. We focus on two applications of preventive medicine: childhood vaccine administration practices in developing countries; and colorectal cancer screening and surveillance. Vaccine administration practices in developing countries suffer from open-vial wastage. Doses remaining from opened vials are disposed at the end of a day, due to lack of appropriate cold storage conditions. We propose administering vaccines from different sizes of multi-dose vials to address the open-vial wastage problem. We utilize a Markov decision process model to maximize the expected total number of doses administered via reducing vaccine wastage. The model dynamically decides which size of a multi-dose vial to open next, and when to terminate vaccination service for the day, given the time remaining in the replenishment cycle and available vaccine stocks. We show that the optimal policies are of control-limit type. Using data for routine pediatric vaccines, we show that the proposed optimal policies could cost-effectively reduce open-vial wastage and significantly improve the covered vaccine demand. We also analyze the initial vaccine inventory composition that specifies how many vials of each size should be kept in stock. We show that the optimal policy for the right vaccine inventory composition may improve the expected vaccine demand covered up to target levels without early termination of vaccination service while realizing reasonably small or no additional cost. Although the number of system variables being tracked in our state space is manageable, the optimal policies still require significant effort to be adopted in practice. That is especially challenging in developing countries, where the resources, e.g., clinic staff, are limited. Therefore, we introduce simple vaccine administration policies that are developed with the guidance of the insights from our numerical and structural analyses. Our insights on the simple vaccine administration policies show that these policies can provide promising performance, in terms of costs and expected vaccine demand covered, compared to the optimal policies while requiring only a single system variable, i.e., time of a decision, to be monitored. Colonoscopy screening prevents, and early-detects colorectal cancer (CRC), one of the most common and deadliest cancers in the world. Considering that the risk of developing CRC significantly increases after age 50, and that the North American population is aging, the colonoscopy screening and follow-up policies employed by gastroenterologists play a vital role in the well-being of the population. Existing clinical guidelines recommend colonoscopy screening policies that are shown to be cost-effective in CRC prevention and early detection. Nevertheless, almost half the practitioners do not follow these guidelines, indicating controversy around the best CRC screening practices. Several studies analyze alternative CRC screening policies using simulation and mathematical models. Especially, dynamic alternative policies, derived by a stochastic dynamic programming approach, can significantly increase health outcome improvements due to CRC screening and follow-up. However, under dynamic policies, colonoscopy screening and surveillance intervals significantly vary in factors such as age, gender, and personal history, which are harder to implement for clinicians. Our study on this second application aims at deriving efficient and simpler-to-implement colonoscopy screening and follow-up policies, but that perform closely to the optimal policies. We employ a patient-level discrete-event simulation model, built and validated using real data, to mimic CRC progression in asymptomatic and higher-risk individuals. We estimate the expected life-years, age-based risk of having CRC, CRC mortality, costs associated with CRC screening, and the number of required colonoscopies for a large set of screening policies. We evaluate the performances of all relevant simpler-to-implement colonoscopy policies, including the periodic screening policies currently used by practitioners, and all feasible periodic policies with n-period switch times (for n=0,1,2). Our analysis identifies under the parameter settings under which alternative and simpler policies are sufficient to provide close-to-optimal performance. These results provide insights on the types of policies on which to focus in future studies, for researchers from both medical and operational research fields