Mechanistic Modeling Approach Research Articles

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology.

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Deciphering oxygen distribution and hypoxia profiles in the tumor microenvironment: a data-driven mechanistic modeling approach

Objective. The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. By utilizing known blood vasculature, we aim to predict hypoxia levels across different tumor types. Approach. Our approach offers a computational method to measure and predict hypoxia using known blood vasculature. By formulating a reaction-diffusion model for oxygen distribution, we derive the corresponding hypoxia profile. Main results. The framework successfully replicates observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor types (breast, ovarian, pancreatic). Additionally, we propose a data-driven method to deduce partial differential equation models with spatially dependent parameters, which allows us to comprehend the variability of hypoxia profiles within tissues. The versatility of our framework lies in capturing diverse and dynamic behaviors of tumor oxygenation, as well as categorizing states of vascularization based on the dynamics of oxygen molecules, as identified by the model parameters. Significance. The proposed data-informed mechanistic method quantitatively assesses hypoxia in the tumor microenvironment by integrating diverse histopathological data and making predictions across different types of data. The framework provides valuable insights from both modeling and biological perspectives, advancing our comprehension of spatio-temporal dynamics of tumor oxygenation.

A mechanistic modelling approach of the host-microbiota interactions to investigate beneficial symbiotic resilience in the human gut.

The health and well-being of a host are deeply influenced by the interactions with its gut microbiota. Contrasted environmental conditions, such as diseases or dietary habits, play a pivotal role in modulating these interactions, impacting microbiota composition and functionality. Such conditions can also lead to transitions from beneficial to detrimental symbiosis, viewed as alternative stable states of the host-microbiota dialogue. This article introduces a novel mathematical model exploring host-microbiota interactions, integrating dynamics of the colonic epithelial crypt, microbial metabolic functions, inflammation sensitivity and colon flows in a transverse section. The model considers metabolic shifts in epithelial cells based on butyrate and hydrogen sulfide concentrations, innate immune pattern recognition receptor activation, microbial oxygen tolerance and the impact of antimicrobial peptides on the microbiota. Using the model, we demonstrated that a high-protein, low-fibre diet exacerbates detrimental interactions and compromises beneficial symbiotic resilience, underscoring a destabilizing effect towards an unhealthy state. Moreover, the proposed model provides essential insights into oxygen levels, fibre and protein breakdown, and basic mechanisms of innate immunity in the colon and offers a crucial understanding of factors influencing the colon environment.

Comparing the efficacy of two generations of EGFR-TKIs: An integrated drug-disease mechanistic model approach in EGFR-mutant lung adenocarcinoma.

e20578 Background: The epidermal growth factor receptor (EGFR) is frequently mutated in patients with lung adenocarcinoma (LUAD), leading to its constitutive activation and subsequent uncontrolled cell proliferation. To counteract this, tyrosine kinase inhibitors (TKIs) targeting EGFR have been developed. However, the emergence of metastases and resistance mutations often undermines the durability of the treatment response. Knowledge-based mechanistic models that replicate existing clinical trial outcomes, tailored to specific population characteristics, provide invaluable assistance in designing future clinical trials. Methods: We have developed physiologically based pharmacokinetic (PBPK) models for gefitinib and osimertinib, two EGFR TKIs, to accurately simulate the drugs' distribution within the primary tumor and metastatic sites following administration. These models were integrated with a mechanistic model of EGFR-mutant LUAD, enabling the representation of the mechanism of action of gefitinib and osimertinib. This model outputs the evolutions of the primary tumor and each metastasis, facilitating the evaluation of patient progression following RECIST criteria. Notably, the model encapsulates the heterogeneity within the tumor, representing various subclones defined by unique mutation profiles, thereby reflecting differential responses to treatment. Calibration of the model was achieved using publicly available data from the NEJ002, FLAURA and AURA3 clinical trials. Rigorous visual predictive checks and statistical tests were employed to ensure the proper behavior of the model. Results: The model adeptly replicated the time to progression in EGFR-mutant LUAD patients receiving gefitinib or osimertinib, whether as first-line or second-line therapy. Furthermore, it accurately mirrored the progression causes as per the RECIST criteria, including the emergence of new metastases in the lung, brain, liver, and bone. With a consistent virtual population as a reference, the combined model facilitated a comparative analysis of the efficacy of both treatments, thereby underscoring its utility in evaluating therapeutic strategies. Conclusions: The faithful replication of real-world data significantly enhances the credibility of our model, rendering it a promising tool for deriving pertinent insights and informing treatment strategies. Following its consecutive prospective validations in the FLAURA2 and MARIPOSA trials, the model is poised to facilitate the generation of synthetic control arms in future clinical trials. This advancement promises a more nuanced analysis of covariate relationships, especially when comparing investigational treatments to established standards of care. Additionally, it potentially reduces the requisite patient cohort size in such trials, optimizing resource utilization.

Metabolic plasticity drives mismatches in physiological traits between prey and predator

Metabolic rate, the rate of energy use, underpins key ecological traits of organisms, from development and locomotion to interaction rates between individuals. In a warming world, the temperature-dependence of metabolic rate is anticipated to shift predator-prey dynamics. Yet, there is little real-world evidence on the effects of warming on trophic interactions. We measured the respiration rates of aquatic larvae of three insect species from populations experiencing a natural temperature gradient in a large-scale mesocosm experiment. Using a mechanistic model we predicted the effects of warming on these taxa’s predator-prey interaction rates. We found that species-specific differences in metabolic plasticity lead to mismatches in the temperature-dependence of their relative velocities, resulting in altered predator-prey interaction rates. This study underscores the role of metabolic plasticity at the species level in modifying trophic interactions and proposes a mechanistic modelling approach that allows an efficient, high-throughput estimation of climate change threats across species pairs.

#894 Dynamic multi-omics and mechanistic modelling approach provides deeper insight into kidney fibrosis progression

Abstract Background and Aims Chronic kidney disease (CKD) affects more than 10% of the world's population and causes millions of deaths annually. Organ fibrosis is the main driver of CKD, leading to excessive accumulation of extracellular matrix (ECM). The presence of myofibroblasts correlates with disease progression. The pro-fibrotic responses of these cells, such as ECM production, are driven by the master regulator TGF-β. Despite progress in understanding the disease, there are no treatments available, and current diagnostic indicators neither facilitate early detection of CKD nor correlate with actual renal damage [1–4] Using human kidney-derived PDGFRβ+ mesenchymal cells we aim to provide time-resolved mechanistic insight into CKD progression. Method Our goal is to understand and model how these cells contribute to fibrosis by studying their kinetic response to TGF-β. Therefore, we use multiple omics measurements: transcriptomics, secretomics and phospho-/proteomics coupled with quantitative imaging of the ECM. For each time point, we quantified differential changes in omics and estimated the activities of transcription factors, kinases and phosphatases [5]. Furthermore, we used causal reasoning to draw mechanistic relationships between omics layers within and across time points. Results For validation, we focused on a set of transcription factors that also showed importance in the network context that considers several data modalities. Using knock-down experiments, we show the relevance of these transcription factors for the ECM deposition as well as the fibrotic response to TGF-β. Conclusion Taken together, by applying mechanistic modeling and the integration of multiple datasets, we are contributing to a better understanding of the factors and mechanisms driving kidney fibrosis. Furthermore, we will publish this dataset and thus provide a valuable resource for researchers in the field of CKD.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Development of Mechanistic In Vitro-In Vivo Extrapolation to Support Bioequivalence Assessment of Long-Acting Injectables.

Long-acting injectable (LAI) formulations provide sustained drug release over an extended period ranging from weeks to several months to improve efficacy, safety, and compliance. Nevertheless, many challenges arise in the development and regulatory assessment of LAI drug products due to a limited understanding of the tissue response to injected particles (e.g., inflammation) impacting in vivo performance. Mechanism-based in silico methods may support the understanding of LAI-physiology interactions. The objectives of this study were as follows: (1) to use a mechanistic modeling approach to delineate the in vivo performance of DepoSubQ Provera® and formulation variants in preclinical species; (2) to predict human exposure based on the knowledge gained from the animal model. The PBPK model evaluated different elements involved in LAI administration and showed that (1) the effective in vivo particle size is potentially larger than the measured in vitro particle size, which could be due to particle aggregation at the injection site, and (2) local inflammation is a key process at the injection site that results in a transient increase in depot volume. This work highlights how a mechanistic modeling approach can identify critical physiological events and product attributes that may affect the in vivo performance of LAIs.

Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug-Disease Mechanistic Model Approach in EGFR-Mutated Lung Adenocarcinoma.

Mutationsin epidermal growth factor receptor (EGFR) are found in approximately 48% of Asian and 19% of Western patients with lung adenocarcinoma (LUAD), leading to aggressive tumor growth. While tyrosine kinase inhibitors (TKIs) like gefitinib and osimertinib target this mutation, treatments often face challenges such as metastasis and resistance. To address this, we developed physiologically based pharmacokinetic (PBPK) models for both drugs, simulating their distribution within the primary tumor and metastases following oral administration. These models, combined with a mechanistic knowledge-based disease model of EGFR-mutated LUAD, allow us to predict the tumor's behavior under treatment considering the diversity within the tumor cells due to different mutations. The combined model reproduces the drugs' distribution within the body, as well as the effects of both gefitinib and osimertinib on EGFR-activation-induced signaling pathways. In addition, the disease model encapsulates the heterogeneity within the tumor through the representation of various subclones. Each subclone is characterized by unique mutation profiles, allowing the model to accurately reproduce clinical outcomes, including patients' progression, aligning with RECIST criteria guidelines (version 1.1). Datasets used for calibration came from NEJ002 and FLAURA clinical trials. The quality of the fit was ensured with rigorous visual predictive checks and statistical tests (comparison metrics computed from bootstrapped, weighted log-rank tests: 98.4% (NEJ002) and 99.9% (FLAURA) similarity). In addition, the model was able to predict outcomes from an independent retrospective study comparing gefitinib and osimertinib which had not been used within the model development phase. This output validation underscores mechanistic models' potential in guiding future clinical trials by comparing treatment efficacies and identifying patients who would benefit most from specific TKIs. Our work is a step towards the design of a powerful tool enhancing personalized treatment in LUAD. It could support treatment strategy evaluations and potentially reduce trial sizes, promising more efficient and targeted therapeutic approaches. Following its consecutive prospective validations with the FLAURA2 and MARIPOSA trials (validation metrics computed from bootstrapped, weighted log-rank tests: 94.0% and 98.1%, respectively), the model could be used to generate a synthetic control arm.

Impacts of behaviour and acclimation of metabolic rate on energetics in sheltered ectotherms: a climate change perspective.

Many ectothermic organisms counter harsh abiotic conditions by seeking refuge in underground retreats. Variations in soil hydrothermal properties within these retreats may impact their energy budget, survival and population dynamics. This makes retreat site choice a critical yet understudied component of their strategies for coping with climate change. We used a mechanistic modelling approach to explore the implications of behavioural adjustments and seasonal acclimation of metabolic rate on retreat depth and the energy budget of ectotherms, considering both current and future climate conditions. We used a temperate amphibian, the alpine newt (Ichthyosaura alpestris), as a model species. Our simulations predict an interactive influence of different thermo- and hydroregulatory strategies on the vertical positioning of individuals in underground refuges. The adoption of a particular strategy largely determines the impact of climate change on retreat site choice. Additionally, we found that, given the behavioural thermoregulation/hydroregulation and metabolic acclimation patterns considered, behaviour within the retreat has a greater impact on ectotherm energetics than acclimation of metabolic rate under different climate change scenarios. We conclude that further empirical research aimed at determining ectotherm behavioural strategies during both surface activity and inactivity is needed to understand their population dynamics and species viability under climate change.

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions.

Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions. With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late stage drug development and is often utilized to support regulatory packages for new drug applications. This minireview will briefly describe the approaches to predicting DDI utilizing PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. Significance Statement PBPK models have been a key tool in the characterization of various pharmacokinetic phenomenon, including drug-drug interactions. This minireview will highlight recent advancements and publications around PBPK DDI modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Facilitation and competition deconstructed: a mechanistic modelling approach to the stress gradient hypothesis applied to drylands

Facilitative interactions among species are key in plant communities. While experimental tests support the Stress Gradient Hypothesis (SGH) as an association between facilitation and stress, whether the shape of net effects along stress gradients can be predicted is controversial, with no available mathematical modelling approaches. We proposed a novel test, using a modification of the R* model to study how negative and positive partial effects of plant interactions in drylands combine along two common stress gradients. We modelled different interactions: competition for water and light, amelioration of soil infiltration and/or grazing protection, obtaining that intensity and importance of facilitation did not generally increase along stress gradients, being dependent on the interaction type. While along the water stress gradient net interactions became more positive, reaching a maximum and then waning again, various outcomes were observed along the grazing gradient. Shape variety was mainly driven by the various shapes of the partial positive effects. Under resource stress, additive interaction effects can be expected, whereas when including grazing, the effects were non-additive. In the context of the SGH, deconstructing the effect of positive and negative interaction in a pairwise mechanistic models of drylands does not show a unique shape along stress gradients.

A mechanistic model approach to characterize suitable regions for Salmo salar aquaculture in the Yellow Sea under global warming

Global warming has a profound effect on aquaculture. Salmon aquaculture, as an emerging industry in China, is particularly vulnerable to climate change. For this reason, spatial planning with mechanistic understanding is essential for mitigating and adapting to the impacts of climate change. Through the use of dynamic energy budget models for Atlantic salmon (Salmo salar) with fine-scale environmental data under 0 m, 20 m, 40 m, and 60 m depth, this study established a framework for identifying optimal regions for salmon aquaculture in the Yellow Sea under current and two Shared Socioeconomic Pathway scenarios (SSP1-2.6 and SSP5-8.5) in the 2050s. These results suggest that most regions in the Yellow Sea under 40 m and 60 m depth are suitable for salmon farming at both current and future scenarios, the central region of the Yellow Sea under 20 m (38°N, 123°E) is the optimal site for salmon aquaculture in China. Overall, these findings provide valuable support for aquaculture management and can help stakeholders create an effective blueprint for adaptation strategies.

Temporal covariance of spatial soil moisture variations: A mechanistic error modeling approach

AbstractWhen estimating field‐scale average soil moisture from sensors measuring at fixed positions, spatial variability in soil moisture leads to “measurement errors” of the spatial mean, which may persist over time due to persistent soil moisture patterns resulting in autocorrelated measurement errors. The uncertainty of parameters that are derived from such measurements may be underestimated when they are assumed to be independent. Temporal autocorrelation models assume stationary random errors, but such error models are not necessarily applicable to soil moisture measurements. As an alternative, we propose a mechanistic error model that is based on the spatial variability of the water retention curve and assumes a uniform water potential. We tested whether spatial soil moisture variability and its temporal covariance could be predicted based on (1) mean soil moisture, (2) water retention variability, and (3) (co)variances of the van Genuchten parameters using a first‐order expansion of the retention curve. The proposed models were tested in a numerical and a field experiment. For the field experiment, in situ sensor measurements and water retention curves were obtained in a field plot. Both experiments showed that water retention variability under a uniform water potential is a good predictor for spatial soil moisture variability, and that soil moisture errors are strongly correlated in time and neglecting them would be an incorrect assumption. The temporal error covariance could be predicted as a function of the mean moisture contents at two observation times. Further research is required to assess the impact of these temporal correlations on soil moisture predictions.

(Invited) Effect of Temperature on Lithium-Ion Battery Voltage Response and How to Model It in the Mechanistic Modeling Approach

The ongoing usage of lithium-ion cells and packs in both stationary and mobile applications necessitates comprehensive tools and models to diagnose and prognose cell performance onboard. One of the main tasks of a battery management system is to infer the state of charge (SOC) and state of health (SOH) of batteries. SOC estimation is usually done by relating a measured relaxation voltage to a stored SOC versus open circuit voltage (OCV) curve. The latter is dependent on the cell SOH and its degradation path. Therefore, both an accurate OCV and relaxation measurement are necessary for state estimation and it is essential to consider how they vary with temperature to ensure accuracy.This study aims at an exploration of the impact of temperature on relaxation and cell’s kinetics and how the voltage response at different temperatures can be simulated using the mechanistic modeling framework by tuning two parameters, the ohmic resistance increase and the rate degradation factor. The mechanistic modeling framework is using electrode half-cell data to synthesize the terminal voltage of battery cells based on the individual electrode potentials. Although proven valid in the quantification of degradation modes, path dependence of aging and cell-to-cell variations, the application of this modeling framework has been restricted so far mostly to voltage profiles recorded at room temperature and low rates. To be used in deployed battery management systems, this limitation should preferably be overcome to enable on-board diagnostics from medium to high rates and at any temperature.

Accounting for the topology of road networks to better explain human‐mediated dispersal in terrestrial landscapes

Human trade and movements are central to biological invasions worldwide. Human activities not only transport species across biogeographical barriers, but also accelerate their post‐introduction spread in the landscape. Thus, by constraining human movements, the spatial structure of road networks might greatly affect the regional spread of invasive species. However, few invasion models have accounted for the topology of road networks so far, and its importance for explaining the regional distribution of invasive species remains mostly unexplored. To address this issue, we developed a spatially explicit and mechanistic human‐mediated dispersal model that accounts and tests for the influence of transport networks on the regional spread of invasive species. Using as a model the spread of the invasive ant Lasius neglectus in the middle Rhône valley (France), we show that accounting for the topology of road networks improves our ability to explain the current distribution of the invasive ant. In contrast, we found that using human population density as a proxy for the frequency of transport events decreases models' performance and might thus not be as appropriate as previously thought. Finally, by differentiating road networks into sub‐networks, we show that national and regional roads are more important than smaller roads for explaining spread patterns. Overall, our results demonstrate that the topology of transport networks can strongly bias regional invasion patterns and highlight the importance of better incorporating it into future invasion models. The mechanistic modelling approach developed in this study should help invasion scientists explore how human‐mediated dispersal and topography shape invasion dynamics in landscapes. Ultimately, our approach could be combined with demographic, natural dispersal and environmental suitability models to refine spread scenarios and improve invasive species monitoring and management at regional to national scales.Keywords: biological invasions, human‐mediated dispersal, road network, secondary spread, spatially explicit model, stochastic jump model

Predictive mechanistic modeling of loading and elution in protein A chromatography

Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and ∼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review.

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

The Applicability of Chromatographic Retention Modeling on Chiral Stationary Phases in Reverse-Phase Mode: A Case Study for Ezetimibe and Its Impurities.

Mechanistic modeling is useful for predicting and modulating selectivity even in early chromatographic method development. This approach is also in accordance with current analytical quality using design principles and is highly welcomed by the authorities. The aim of this study was to investigate the separation behavior of two different types of chiral stationary phases (CSPs) for the separation of ezetimibe and its related substances using the mechanistic retention modeling approach offered by the Drylab software (version 4.5) package. Based on the obtained results, both CSPs presented with chemoselectivity towards the impurities of ezetimibe. The cyclodextrin-based CSP displayed a higher separation capacity and was able to separate seven related substances from the active pharmaceutical ingredient, while the cellulose-based column enabled the baseline resolution of six impurities from ezetimibe. Generally, the accuracy of predicted retention times was lower for the polysaccharide CSP, which could indicate the presence of additional secondary interactions between the analytes and the CSP. It was also demonstrated that the combination of mechanistic modeling and an experimental design approach can be applied to method development on CSPs in reverse-phase mode. The applicability of the methods was tested on spiked artificial placebo samples, while intraday and long-term (2 years) method repeatability was also challenged through comparing the obtained retention times and resolution values. The results indicated the excellent robustness of the selected setpoints. Overall, our findings indicate that the chiral columns could offer orthogonal selectivity to traditional reverse-phase columns for the separation of structurally similar compounds.

Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia.

Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.