Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy

A Role of Effector CD 8 + T Cells Against Circulating Tumor Cells Cloaked with Platelets: Insights from a Mathematical Model

Cancer metastasis accounts for a majority of cancer-related deaths worldwide. Metastasis occurs when the primary tumor sheds cells into the blood and lymphatic circulation, thereby becoming circulating tumor cells (CTCs) that transverse through the circulatory system, extravasate the circulation and establish a secondary distant tumor. Accumulating evidence suggests that circulating effector CD 8 + T cells are able to recognize and attack arrested or extravasating CTCs, but this important antitumoral effect remains largely undefined. Recent studies highlighted the supporting role of activated platelets in CTCs's extravasation from the bloodstream, contributing to metastatic progression. In this work, a simple mathematical model describes how the primary tumor, CTCs, activated platelets and effector CD 8 + T cells participate in metastasis. The stability analysis reveals that for early dissemination of CTCs, effector CD 8 + T cells can present or keep secondary metastatic tumor burden at low equilibrium state. In contrast, for late dissemination of CTCs, effector CD 8 + T cells are unlikely to inhibit secondary tumor growth. Moreover, global sensitivity analysis demonstrates that the rate of the primary tumor growth, intravascular CTC proliferation, as well as the CD 8 + T cell proliferation, strongly affects the number of the secondary tumor cells. Additionally, model simulations indicate that an increase in CTC proliferation greatly contributes to tumor metastasis. Our simulations further illustrate that the higher the number of activated platelets on CTCs, the higher the probability of secondary tumor establishment. Intriguingly, from a mathematical immunology perspective, our simulations indicate that if the rate of effector CD 8 + T cell proliferation is high, then the secondary tumor formation can be considerably delayed, providing a window for adjuvant tumor control strategies. Collectively, our results suggest that the earlier the effector CD 8 + T cell response is enhanced the higher is the probability of preventing or delaying secondary tumor metastases.

Longitudinal study on blood and biochemical indexes of Tibetan and Han in high altitude area

Objective

This study aims to review the blood routine and biochemical indicators of the plateau population for three consecutive years, and analyze the impact of the plateau on these blood indicators of the Tibetan population and the Han immigrant population.

Method

These parameters were extracted from the Laboratory Department of Ali District People's Hospital in Tibet from January 2019 to December 2021, including blood routine, liver and kidney function, blood lipids, myocardial enzyme spectrum, and rheumatic factor indicators. Changes in these parameters were analyzed over 3 consecutive years according to inclusion and exclusion criteria.

Result

A total of 114 Tibetans and 93 Hans participated in the study. These parameters were significantly different between Tibetan and Han populations. Red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), mean hemoglobin content (MCH), mean corpuscular hemoglobin concentration (MCHC), white blood cells (WBC), lymphocytes (LYMPH) and monocytes (MONO) were significantly higher in Hans than Tibetans (p < 0.05). Biochemically, total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB), urea nitrogen (Urea), creatinine (Cr), uric acid (UA), glucose (GLU), triglycerides (TG) and creatine kinase isoenzyme (CKMB) were significantly higher in Hans than Tibetans; aspartate aminotransferase (AST), glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), antistreptolysin (ASO), and C-reactive protein (CRP) were significantly higher in Tibetans than Hans (p < 0.05). There were no obvious continuous upward or downward trend of the parameters for 3 consecutive years.

Conclusion

In high-altitude areas, Han immigrants have long-term stress changes compared with Tibetans. The main differences are reflected in the blood system, liver and kidney functions, etc., which provide basic data for further research on the health status of plateau populations.

An overview and a roadmap for artificial intelligence in hematology and oncology

BACKGROUND

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals.

METHODS

In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology.

RESULTS

First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology.

CONCLUSION

Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

Individual hematotoxicity prediction of further chemotherapy cycles by dynamic mathematical models in patients with gastrointestinal tumors

PURPOSE

Hematotoxicity is a common side-effect of cytotoxic gastrointestinal (GI) cancer therapies. An unsolved problem is to predict the individual risk therefore to decide on treatment adaptions. We applied an established biomathematical prediction model and primarily evaluated its predictive value in patients undergoing chemotherapy for GI cancers in curative intent.

METHODS

In a prospective, observational multicenter study on patients with gastro-esophageal or pancreatic cancer (n = 28) receiving myelosuppressive adjuvant or neoadjuvant chemotherapy (FLO(T) or FOLFIRINOX), individual model parameters were learned based on patients' observed laboratory values during the first chemotherapy cycle and further external data resources. Grades of hematotoxicity of subsequent cycles were predicted by model simulation and compared with observed data.

RESULTS

The most common high-grade hematological toxicity was neutropenia [19/28 patients (68%)]. For the FLO(T) regimen, individual grades of thrombocytopenia and leukopenia could be well predicted for cycles 2-4, as well as grades of neutropenia for cycle 2. Prediction accuracy for neutropenia in the third and fourth cycle differed by one toxicity grade on average. For the FOLFIRINOX-regimen, thrombocytopenia predictions showed a maximum deviation of one toxicity grade up to the end of therapy (8 cycles). Deviations of predictions were less than one degree on average up to cycle 4 for neutropenia, and up to cycle 6 for leukopenia.

CONCLUSION

The biomathematical model showed excellent short-term and decent long-term prediction performance for all relevant hematological side effects associated with FLO(T)/FOLFIRINOX. Clinical utility of this precision-medicine approach needs to be further investigated in a larger cohort.

Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

Cytotoxic cancer therapy often results in dose-limiting haematotoxic side effects. Predicting an individual's risk is a major objective in precision medicine of cancer treatment. In this regard, patient heterogeneity presents a significant challenge. In this paper, we explore the use of hypothesis-free machine learning models based on recurrent nonlinear auto-regressive networks with exogenous inputs (NARX) as an approach to achieve this goal. Also, we propose a knowledge transfer approach to ameliorate the issue of sparse individual data, which typically hampers learning of individual networks. We demonstrate the feasibility of our approach based on a virtual patient population generated using a semi-mechanistic model of haematopoiesis and imposing different cytotoxic therapy scenarios on it. Employing different techniques of model optimisation, we derive robust and parsimonious individual networks with good generalisation performances. Moreover, we analyse in detail possible factors influencing the generalisation performance. Results suggest that our transfer learning approach using NARX networks can provide robust predictions of individual patient's response to treatment. As a practical perspective, we apply our approach to individual time series data of two patients with non-Hodgkin's lymphoma.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

On the Parametrization of Epidemiologic Models—Lessons from Modelling COVID-19 Epidemic

Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models.

Exposure-response analysis of adverse events associated with molibresib and its active metabolites in patients with solid tumors

Molibresib (GSK525762) is an investigational orally bioavailable small‐molecule bromodomain and extraterminal (BET) protein inhibitor for the treatment of advanced solid tumors. In the first‐time‐in‐human BET115521 study of molibresib in patients with solid tumors, thrombocytopenia was the most frequent treatment‐related adverse event (AE), QT prolongation was an AE of special interest based on preclinical signals, and gastrointestinal (GI) AEs (nausea, vomiting, diarrhea, and dysgeusia) were often observed. The aims of this analysis were the following: (i) develop a population pharmacokinetic (PK)/pharmacodynamic (PD) model capable of predicting platelet time courses in individual patients after administration of molibresib and identify covariates of clinical interest; (ii) evaluate the effects of molibresib (and/or its two active metabolites [GSK3529246]) exposure on cardiac repolarization by applying a systematic modeling approach using high‐quality, intensive, PK time‐matched 12‐lead electrocardiogram measurements; (iii) evaluate the exposure–response (ER) relationship between molibresib and/or GSK3529246 exposures and the occurrence of Grade 2 or higher GI AEs. Overall, the PK/PD model (including a maximal drug effect model and molibresib concentration) adequately described platelet counts following molibresib treatment and was used to simulate the impact of molibresib dosing on thrombocytopenia at different doses and regimens. ER analyses showed no clinically meaningful QT interval prolongation with molibresib at up to 100 mg q.d., and no strong correlation between molibresib exposure and the occurrence of Grade 2 or higher GI AEs. The models described here can aid dosing/schedule and drug combination strategies and may support a thorough QT study waiver request for molibresib.

Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples

Quantitative Systems Toxicology (QST) models, recapitulating pharmacokinetics and mechanism of action together with the organic response at multiple levels of biological organization, can provide predictions on the magnitude of injury and recovery dynamics to support study design and decision-making during drug development. Here, we highlight the application of QST models to predict toxicities of cancer treatments, such as cytopenia(s) and gastrointestinal adverse effects, where narrow therapeutic indexes need to be actively managed. The importance of bifurcation analysis is demonstrated in QST models of hematologic toxicity to understand how different regions of the parameter space generate different behaviors following cancer treatment, which results in asymptotically stable predictions, yet highly irregular for specific schedules, or oscillating predictions of blood cell levels. In addition, an agent-based model of the intestinal crypt was used to simulate how the spatial location of the injury within the crypt affects the villus disruption severity. We discuss the value of QST modeling approaches to support drug development and how they align with technological advances impacting trial design including patient selection, dose/regimen selection, and ultimately patient safety.

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

AIMS

Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes.

METHODS

We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles.

RESULTS

The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration.

CONCLUSION

We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making.

Flow-accelerated platelet biogenesis is due to an elasto-hydrodynamic instability

Blood platelets are formed by fragmentation of long membrane extensions from bone marrow megakaryocytes in the blood flow. Using lattice-Boltzmann/immersed boundary simulations we propose a biological Rayleigh-Plateau instability as the biophysical mechanism behind this fragmentation process. This instability is akin to the surface tension-induced breakup of a liquid jet but is driven by active cortical processes including actomyosin contractility and microtubule sliding. Our fully three-dimensional simulations highlight the crucial role of actomyosin contractility, which is required to trigger the instability, and illustrate how the wavelength of the instability determines the size of the final platelets. The elasto-hydrodynamic origin of the fragmentation explains the strong acceleration of platelet biogenesis in the presence of an external flow, which we observe in agreement with experiments. Our simulations then allow us to disentangle the influence of specific flow conditions: While a homogeneous flow with uniform velocity leads to the strongest acceleration, a shear flow with a linear velocity gradient can cause fusion events of two developing platelet-sized swellings during fragmentation. A fusion event may lead to the release of larger structures which are observable as preplatelets in experiments. Together, our findings strongly indicate a mainly physical origin of fragmentation and regulation of platelet size in flow-accelerated platelet biogenesis.

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Background

Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.

Results

In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.

Conclusions

By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.