Data-driven methods for biological modeling in industry

Wednesday, June 16 at 11:30am (PDT)
Wednesday, June 16 at 07:30pm (BST)
Thursday, June 17 03:30am (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "MS14" time block.
Note: this minisymposia has multiple sessions. The second session is MS13-MFBM (click here).

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Kevin Flores (North Carolina State University, USA)


This minisymposium highlights recent advances in data-driven mathematical modeling for biology in industry, including parameter estimation, uncertainty quantification, machine learning, and image analysis. In particular, the emphasis is on the development of methods for overcoming practical challenges encountered with real-world data from industrial applications, such as high levels of observation error, model bias, and intra- as well as inter-individual or experimental heterogeneity. Topics include optimization of clinical dose regimens, optimal sample collection, forecasting, hypothesis testing and/or model selection, and the integration of heterogeneous sources of data from multiple scales and data acquisition platforms.

Richard Allen

(Quantitative Systems Pharmacology, Early Clinical Development, Pfizer Worldwide Research Development and Medical, USA)
"Analyzing and Predicting Clinical Trial Data with Systems Modeling"
Systems modeling approaches have found increasing utility in supporting the discovery and development of novel therapeutics. By capturing key biological interactions and incorporating a wide range of data to inform the model, a systems model can be a powerful tool to design, predict, and analyze clinical trials. However, typical clinical trials show a highly variable response – such that fitting a model to the mean at best might be losing some information, and at worse fully mischaracterizing the response. Conversely, mathematical representations of complex biology lead to large models and associated uncertainty in parameter estimation. In this talk I will introduce how systems modeling is being used in drug discovery and development, and the challenges of such an approach. In particular, I will discuss how we generate virtual patients and populations to explore parameter uncertainty in a model while constraining the response using the observed clinical variability. Furthermore, I will show – by example - how analysis of a virtual population can lead to physiological insights.

Florencio Serrano Castillo

(Clinical Pharmacology, Modelling and Simulations, Amgen Inc., USA)
"Dosing guidance optimization, leveraging real world heterogeneity to forecast clinical biomarker response"
Understanding the dynamic and variability of a drug’s pharmacokinetic (its concentration in the body) and pharmacodynamic (its effect on the body) profiles is of critical importance for the design and success of any clinical study. However, the development, implementation and validation of system-level models that explicitly relate the complex biological mechanisms ruling the relationships between dose, exposure, clinical response and safety of a drug while simultaneously describing clinical variability is often unfeasible due to both technical and logistic limitations. Population pharmacokinetic and pharmacodynamic models (popPK/PD) are a powerful tool to circumvent this limitations. popPK/PD models leverage the inherent nosiness of clinical/biological data to inform statistical models embedded into their core dynamic structure to describe both intrinsic and extrinsic sources of variability representative of the clinical setting. This hierarchical structure can then be leveraged to perform clinical trial simulations that predict the impact of various design options and thus inform strategic decisions throughout the development and life cycle management of a therapeutic. This talk will provide a general example of how to leverage a pseudo-mechanistic popPKPD to identify complex patterns between highly heterogeneous clinical dose and biomarker data in order to provide guidance regarding the feasibility and uncertainty associated with various proposed clinical scenarios. Furthermore, it will also describe methodologies on how to address typical challenges with this process, such as the validation of a previously developed model for a different patient population, the generation and implementation of appropriate clinical trial simulation schemes to address possible population and strategy characteristics, and the collation and interpretation of model-derived outputs in order to inform development strategies.

Zackary Kenz

(DILIsym Services, a Simulations Plus Company, USA)
"Quantitative Systems Pharmacology Modeling of Fibrotic Diseases"
Fibrotic diseases occur in multiple organs, characterized by escalating fibrosis affecting organ function. For example, in idiopathic pulmonary fibrosis (IPF), normal lung is progressively replaced by fibrotic architecture resulting in compromised movement and gas exchange. Similarly, in non-alcoholic steatohepatitis (NASH), normal liver cells are replaced with fibrotic matrix resulting in compromised liver clearance mechanisms. In both cases, there are no cures and few treatment options. These fibrotic diseases represent areas of unmet clinical need, where improved understanding of pathophysiology and treatment interventions could impact the drug development pipeline and patient care. To accelerate the clinical development of treatments in IPF and NASH, DILIsym Services has developed QSP models of each disease state. These models contain mechanistic representations of ongoing injury, inflammation, and accumulation of extracellular matrix, each of which represent potential targets for treatment intervention which can be quantitatively assessed within the model. Further, mechanisms are dynamically linked with clinical outcomes, providing insight across multiple scales from molecular intervention to cellular response to tissue response. Selected portions of the model development and validation will be discussed, along with example treatments. These QSP platforms are available and actively in use to support ongoing development of effective treatments for IPF and NASH patients.

Anna Neely

(TigerRisk Partners, USA)
"Estimating the growing risk of severe thunderstorms"
'Severe weather is one of the biggest drivers of insured catastrophe losses in the US.  Catastrophe models are used to estimate risk of severe weather conditions - are they aiming at a moving target?  Losses to the insurance industry have increased at a rate of 9% annually since 2000.  Far outpacing expectations.  In this talk we'll dive into some of the drivers of this increase and de-mystify some insurance industry folklore.'

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.