scholarly journals Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4448
Author(s):  
Sophia Belkhir ◽  
Frederic Thomas ◽  
Benjamin Roche
Keyword(s):  
Mathematical Modeling ◽  
Evolutionary Dynamics ◽  
Treatment Strategies ◽  
Clinical Trial Design ◽  
Volterra Equations ◽  
Treatment Resistant ◽  
Cancer Treatments ◽  
Treatment Benefits ◽  
Adaptive Therapy ◽  
Resistant Cells

One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka–Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design.

Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer (mCRPC): Updated analysis of the adaptive abiraterone (abi) study (NCT02415621).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 5041-5041 ◽  
Author(s):  
Jingsong Zhang ◽  
Mayer N. Fishman ◽  
Joel Brown ◽  
Robert A Gatenby
Keyword(s):  
Prostate Cancer ◽  
Cancer Progression ◽  
Evolutionary Dynamics ◽  
Radiographic Progression ◽  
Evolutionary Game ◽  
Volterra Equations ◽  
Drug Usage ◽  
Initial Finding ◽  
Frontline Therapy ◽  
Adaptive Therapy

5041 Background: To achieve better prostate cancer control and to delay the emergency of treatment resistance, we developed an evolutionary game theory model using Lotka-Volterra equations with three competing prostate cancer "species": T+, Tp, and T-. T+ prostate cancer cells depend on exogenous androgen; Tp cells express CYP17A1, produce and depend on androgen; and T- cells are androgen-independent and abi-resistant. We applied this model to guide the on and off treatment cycles with abi for mCRPC. At the first interim analysis with 11 patients, this approach was shown to prolong the time to cancer progression with less than 50% drug usage compared to the conventional continuous Abi ( Nat Commun. 2017). Here we present the updated data of this phase 2 study. Methods: Men with asymptomatic or minimal symptomatic mCRPC were enrolled after they achieved > 50% PSA reduction with abi as a frontline therapy for mCRPC. The primary objective is feasibility and is measured by the percentage of abi responsive men who remain to be responsive to abi (defined as > 50% decline of the pre Abi PSA) after completing 2 adaptive treatment cycles. The secondary objective is to assess the clinical benefits by comparing the radiographic progression free survival (rPFS) in men undergoing adaptive Abi therapy to the historical AA 302 trial. Results: At the data cut off in Jan 2019, the study has completed enrollment for the non-African American cohort. 15 enrolled men had > 11 months of follow up. All 15 men were off Abi for at least 3 months before abi was restarted for PSA progression at cycle 1. Seven out of the 15 men had completed at least 2 adaptive therapy cycles. Four of the rest 8 men remained on study and have not reached cycle 2. Six men were off study due to radiographic progression at month 11, 20.4, 30, 30.5, 38 and 53 from their first dose of Abi. Compare to the 16.2 months median rPFS in the AA 302 trial, the median rPFS of the 15 men would be no less than 30 months (p = 0.0068, Fisher’s exact test). Their average usage of Abi was 49% of the continuous Abi. Conclusions: Adaptive Abi therapy is feasible in men who responded to Abi as a frontline therapy for mCRPC. The updated data are consistent with our initial finding that our adaptive therapy approach can prolong the time to cancer progression with less than 50% drug usage compared to the conventional continuous Abi. Clinical trial information: NCT02415621.

scholarly journals 116 Retrospective Analysis of Clozapine Augmentation in Treatment-Resistant Schizophrenia in an Outpatient Setting

CNS Spectrums ◽  
2018 ◽  
Vol 23 (1) ◽  
pp. 73-74
Author(s):  
Charles Odom ◽  
Frozan Walyzada ◽  
Pankaj Manocha ◽  
Monika Gashi ◽  
Ashaki Martin ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Treatment Options ◽  
D2 Receptor ◽  
Treatment Strategies ◽  
Outpatient Setting ◽  
Mood Stabilizers ◽  
Adjunctive Treatment ◽  
Supporting Evidence ◽  
Treatment Resistant ◽  
Augmentation Strategies

AbstractStudy ObjectivesThis retrospective analysis hopes to add to the literature about Treatment Resistant Schizophrenia (TRS), augmentation strategies with antipsychotics used in our patient population with the hopes of clarifying what possibilities should be further studied. In addition, we aim to emphasize the need for focusing on individualized treatment and multidisciplinary efforts to ensure compliance and appropriate disposition options.MethodWe reviewed retrospectively 3025 charts of patients between January 2017 to March 2017 in our outpatient department establishing which antipsychotic clozapineaugmentation strategies were being used. We also did a literature review to establish what augmentation strategies are recommended. These patients will then be compared to a random sample of patients in the clinic who were not prescribed clozapine and compared for readmission rate, side effect profile, length of stay while admitted, frequency of clinic attendance and compliance with outpatient appointments.ResultsOut of 3025 patients 35 were prescribed Clozapine as monotherapy and 5 patients had clozapine plus psychopharmacological augmentation. Ages ranged from 21-86. Out of the 39 patients, there were 13 male and 26 female. The predominant diagnosis was mood disorder or MDD with psychotic features followed by schizophrenia. The augmentation antipsychotics used were aripiprazole and risperidone. In the literature, the most frequent augmentation strategy for TRS is adding another antipsychotic with more D2 receptor blockade. Other strategies involve identifying and treating the symptoms not controlled by clozapine.ConclusionsCurrently augmentation of Clozapine in TRS is highly individualized due to lack of supporting evidence to state the contrary. When working with treatmentresistant patients who are not responding to clozapine alone, it is imperative to thoroughly review and consider all treatment options and augmentation strategies. More studies should be done in controlled settings to better evaluate possibilities as well as more evaluations to be done on other ways of augmentation of clozapine. Literature has stated between 20-60% of patients are defined as TRS. Clozapine is considered as one of the most effective treatment available at present time for TRS. Recent literature suggests despite its superior efficacy, as many as 70% of those suffering from TRS on clozapine continue to suffer from positive, negative or cognitive symptoms. The literature has abundant adjunctive treatment strategies such as the addition of antipsychotics, mood stabilizers, antidepressants, or even with the use of electroconvulsive therapy. We emphasize the importance of correctly identifying TRS patients who may benefit from the initiation of clozapine, what would be beneficial for them if they do not respond, how to tailor their treatment to target symptoms not being ameliorated, and recommend treatment in these complex cases be multidisciplinary.Funding AcknowledgementsNo funding.

scholarly journals A Perspective on Expanding Our Understanding of Cancer Treatments by Integrating Approaches from the Biological and Physical Sciences

2020 ◽  
Vol 25 (7) ◽  
pp. 672-683
Author(s):  
Emma J. Fong ◽  
Carly Strelez ◽  
Shannon M. Mumenthaler
Keyword(s):  
Cancer Research ◽  
Cancer Progression ◽  
Adaptive System ◽  
Complex Adaptive System ◽  
Treatment Strategies ◽  
Model Systems ◽  
Physical Sciences ◽  
Cancer Treatments ◽  
High Content Imaging ◽  
Complex Adaptive

Multicellular systems such as cancer suffer from immense complexity. It is imperative to capture the heterogeneity of these systems across scales to achieve a deeper understanding of the underlying biology and develop effective treatment strategies. In this perspective article, we will discuss how recent technologies and approaches from the biological and physical sciences have transformed traditional ways of measuring, interpreting, and treating cancer. During the SLAS 2019 Annual Meeting, SBI2 hosted a Special Interest Group (SIG) on this topic. Academic and industry leaders engaged in discussions surrounding what biological model systems are appropriate to study cancer complexity, what assays are necessary to interrogate this complexity, and how physical sciences approaches may be useful to detangle this complexity. In particular, we examined the utility of mathematical models in predicting cancer progression and treatment response when tightly integrated with reproducible, quantitative, and dynamic biological measurements achieved using high-content imaging and analysis. The dialogue centered around the impetus for convergent biosciences, bringing new perspectives to cancer research to further understand this complex adaptive system and successfully intervene therapeutically.

scholarly journals Oral esketamine for treatment-resistant depression: rationale and design of a randomized controlled trial

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Sanne Y. Smith-Apeldoorn ◽  
Jolien K. E. Veraart ◽  
Jeanine Kamphuis ◽  
Antoinette D. I. van Asselt ◽  
Daan J. Touw ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Treatment Strategies ◽  
Repeated Administration ◽  
Additional Treatment ◽  
Racemic Mixture ◽  
Treatment Resistant Depression ◽  
Treatment Resistant ◽  
Routes Of Administration ◽  
Antidepressant Efficacy ◽  
Resistant Depression

Abstract Background There is an urgent need to develop additional treatment strategies for patients with treatment-resistant depression (TRD). The rapid but short-lived antidepressant effects of intravenous (IV) ketamine as a racemic mixture have been shown repeatedly in this population, but there is still a paucity of data on the efficacy and safety of (a) different routes of administration, and (b) ketamine’s enantiomers esketamine and arketamine. Given practical advantages of oral over IV administration and pharmacodynamic arguments for better antidepressant efficacy of esketamine over arketamine, we designed a study to investigate repeated administration of oral esketamine in patients with TRD. Methods This study features a triple-blind randomized placebo-controlled trial (RCT) comparing daily oral esketamine versus placebo as add-on to regular antidepressant medications for a period of 6 weeks, succeeded by a follow-up of 4 weeks. The methods support examination of the efficacy, safety, tolerability, mechanisms of action, and economic impact of oral esketamine in patients with TRD. Discussion This is the first RCT investigating repeated oral esketamine administration in patients with TRD. If shown to be effective and tolerated, oral esketamine administration poses important advantages over IV administration. Trial registration Dutch Trial Register, NTR6161. Registered 21 October 2016.

scholarly journals Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

2020 ◽  
Vol 117 (32) ◽  
pp. 19455-19464 ◽  
Author(s):  
Helen K. Alexander ◽  
R. Craig MacLean
Keyword(s):  
De Novo ◽  
Single Cells ◽  
Treatment Strategies ◽  
Bacterial Populations ◽  
Resistance Plasmids ◽  
Evolution Of Resistance ◽  
Resistant Strains ◽  
Low Probability ◽  
Emergence Of Resistance ◽  
Resistant Cells

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favored over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using twoPseudomonas aeruginosastrains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as one-eighth of the resistant strain’s minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.

scholarly journals Cost analysis of treatment strategies for the control of HSV–2 infection in the U.S.: A mathematical modeling-based case study

2020 ◽  
Vol 324 ◽  
pp. 108347
Author(s):  
Luis Almonte-Vega ◽  
Monica Colón-Vargas ◽  
Ligia Luna-Jarrín ◽  
Joel Martinez ◽  
Jordy Rodriguez-Rinc ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Cost Analysis ◽  
Treatment Strategies ◽  
The U.S

scholarly journals Therapeutically targeting cyclin D1 in primary tumors arising from loss of Ini1

2010 ◽  
Vol 108 (1) ◽  
pp. 319-324 ◽  
Author(s):  
Melissa E. Smith ◽  
Velasco Cimica ◽  
Srinivasa Chinni ◽  
Suman Jana ◽  
Wade Koba ◽  
...  
Keyword(s):  
Cyclin D1 ◽  
Effective Treatment ◽  
Treatment Strategies ◽  
Suppressor Gene ◽  
Therapeutic Strategies ◽  
Genetically Engineered ◽  
Primary Tumors ◽  
Rhabdoid Tumors ◽  
High Level ◽  
Resistant Cells

Rhabdoid tumors (RTs) are rare, highly aggressive pediatric malignancies with poor prognosis and with no standard or effective treatment strategies. RTs are characterized by biallelic inactivation of the INI1 tumor suppressor gene. INI1 directly represses CCND1 and activates cyclin-dependent kinase (cdk) inhibitors p16Ink4a and p21CIP. RTs are exquisitely dependent on cyclin D1 for genesis and survival. To facilitate translation of unique therapeutic strategies, we have used genetically engineered, Ini1+/− mice for therapeutic testing. We found that PET can be used to noninvasively and accurately detect primary tumors in Ini1+/− mice. In a PET-guided longitudinal study, we found that treating Ini1+/− mice bearing primary tumors with the pan-cdk inhibitor flavopiridol resulted in complete and stable regression of some tumors. Other tumors showed resistance to flavopiridol, and one of the resistant tumors overexpressed cyclin D1, more than flavopiridol-sensitive cells. The concentration of flavopiridol used was not sufficient to down-modulate the high level of cyclin D1 and failed to induce cell death in the resistant cells. Furthermore, FISH and PCR analyses indicated that there is aneuploidy and increased CCND1 copy number in resistant cells. These studies indicate that resistance to flavopiridol may be correlated to elevated cyclin D1 levels. Our studies also indicate that Ini1+/− mice are valuable tools for testing unique therapeutic strategies and for understanding mechanisms of drug resistance in tumors that arise owing to loss of Ini1, which is essential for developing effective treatment strategies against these aggressive tumors.

scholarly journals The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells

2015 ◽  
Vol 14s4 ◽  
pp. CIN.S19338 ◽  
Author(s):  
Shannon M. Mumenthaler ◽  
Jasmine Foo ◽  
Nathan C. Choi ◽  
Nicholas Heise ◽  
Kevin Leder ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Evolutionary Dynamics ◽  
Kinase Inhibitors ◽  
Interdisciplinary Approach ◽  
Dynamic Feedback ◽  
Environmental Niche ◽  
Free Environment ◽  
The Impact ◽  
Drug Free ◽  
Resistant Cells

Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of a drug. Using a combined experimental and computational approach, we developed a mathematical model of preexisting drug resistance describing multiple cellular compartments, each representing a specific tumor environmental niche. This model was parameterized using a novel experimental dataset derived from the HCC827 erlotinib-sensitive and -resistant NSCLC cell lines. We found that, in contrast to in the drug-free environment, resistant cells may hold a fitness advantage compared to parental cells in microenvironments deficient in oxygen and nutrients. We then utilized the model to predict the impact of drug and nutrient gradients on tumor composition and recurrence times, demonstrating that these endpoints are strongly dependent on the microenvironment. Our interdisciplinary approach provides a model system to quantitatively investigate the impact of microenvironmental effects on the evolutionary dynamics of tumor cells.

scholarly journals Testing the optimality properties of a dual antibiotic treatment in a two-locus, two-allele model

2014 ◽  
Vol 11 (96) ◽  
pp. 20131035 ◽  
Author(s):  
Rafael Peña-Miller ◽  
Ayari Fuentes-Hernandez ◽  
Carlos Reding ◽  
Ivana Gudelj ◽  
Robert Beardmore
Keyword(s):  
Evolutionary Dynamics ◽  
Control Strategies ◽  
Treatment Strategies ◽  
Heterogeneous Population ◽  
Antimicrobial Drugs ◽  
Population Structures ◽  
Optimality Properties ◽  
Allele Model ◽  
Antibiotic Combination

Mathematically speaking, it is self-evident that the optimal control of complex, dynamical systems with many interacting components cannot be achieved with ‘non-responsive’ control strategies that are constant through time. Although there are notable exceptions, this is usually how we design treatments with antimicrobial drugs when we give the same dose and the same antibiotic combination each day. Here, we use a frequency- and density-dependent pharmacogenetics mathematical model based on a standard, two-locus, two-allele representation of how bacteria resist antibiotics to probe the question of whether optimal antibiotic treatments might, in fact, be constant through time. The model describes the ecological and evolutionary dynamics of different sub-populations of the bacterium Escherichia coli that compete for a single limiting resource in a two-drug environment. We use in vitro evolutionary experiments to calibrate and test the model and show that antibiotic environments can support dynamically changing and heterogeneous population structures. We then demonstrate, theoretically and empirically, that the best treatment strategies should adapt through time and constant strategies are not optimal.

scholarly journals Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations

2014 ◽  
Vol 4 (4) ◽  
pp. 20140037 ◽  
Author(s):  
David Liao ◽  
Thea D. Tlsty
Keyword(s):  
Game Theory ◽  
Population Dynamics ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Treatment Strategies ◽  
Analysis Method ◽  
Analysis Techniques ◽  
Game Theoretic ◽  
And Control

Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.

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