Beyond Deterministic Models in Drug Discovery and Development

Irurzun-Arana, Itziar; Rackauckas, Christopher; McDonald, Thomas O.; Trocóniz, Iñaki F.

doi:10.1016/j.tips.2020.09.005

Cited by 22 publications

(20 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SDEME models are extensions of the frequently used nonlinear mixed effects models with differential equations, where the underlying dynamical system includes stochasticity (e.g., biological and environmental stochastic effects). Thus, SDEME models provide a means to distinguish the following three sources of variability: interindividual variability (IIV), stochasticity in the dynamics (also known as system noise), and measurement noise 16,20 . As this is—to the authors’ best knowledge—the first application of an SDEME model to analyze daily lung function measurements, the model structure was developed with the following criteria in mind: (1) the model should be conceptually simple with interpretable parameters, (2) it should be able to describe the most important stochastic characteristics and trends of the PEF data, and (3) it should be computationally feasible and robust enough to be applied to large sets of clinical trial data.…”

Section: Methodsmentioning

confidence: 99%

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

Leander

Jirstrand

Eriksson

et al. 2021

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Home‐based measures of lung function, inflammation, symptoms, and medication use are frequently collected in respiratory clinical trials. However, new statistical approaches are needed to make better use of the information contained in these data‐rich variables. In this work, we use data from two phase III asthma clinical trials demonstrating the benefit of benralizumab treatment to develop a novel longitudinal mixed effects model of peak expiratory flow (PEF), a lung function measure easily captured at home using a hand‐held device. The model is based on an extension of the mixed effects modeling framework to incorporate stochastic differential equations and allows for quantification of several statistical properties of a patient's PEF data: the longitudinal trend, long‐term fluctuations, and day‐to‐day variability. These properties are compared between treatment groups and related to a patient's exacerbation risk using a repeated time‐to‐event model. The mixed effects model adequately described the observed data from the two clinical trials, and model parameters were accurately estimated. Benralizumab treatment was shown to improve a patient's average PEF level and reduce long‐term fluctuations. Both of these effects were shown to be associated with a lower exacerbation risk. The day‐to‐day variability was neither significantly affected by treatment nor associated with exacerbation risk. Our work shows the potential of a stochastic model‐based analysis of home‐based lung function measures to support better estimation and understanding of treatment effects and disease stability. The proposed analysis can serve as a complement to descriptive statistics of home‐based measures in the reporting of respiratory clinical trials.

show abstract

Section: Methodsmentioning

confidence: 99%

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

Leander

Jirstrand

Eriksson

et al. 2021

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

show abstract

“…There are additional case studies of ML applications in pharmacometrics that do not yet create their own category [59][60][61], including the use of stochastic approaches to predict the effect of random genetic evolution or small populations [62,63]. However, stochastic approaches are more widely used for the generation of virtual patient populations.…”

Section: Stochasticity and Virtual Populationsmentioning

confidence: 99%

Two heads are better than one: current landscape of integrating QSP and machine learning

Zhang

Androulakis

Bonate

et al. 2022

J Pharmacokinet Pharmacodyn

Self Cite

View full text Add to dashboard Cite

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

show abstract

“…Modeling tumor resistance using evolutionary theories is helpful for predicting the resistance trajectories and exploring possibilities to overcome them [ 256 , 258 , 259 ]. Many modeling works characterized cancer genetic and clinical progression as a stochastic process [ 260 ], in which tumor cells proliferate, divide, and apoptosis based on probabilities [ 261 , 262 , 263 , 264 , 265 ]. Dynamic tumor evolution models were developed to predict the treatment responses of therapeutic antibodies [ 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 ].…”

Section: Modeling Pharmacodynamics Of Therapeutic Antibodiesmentioning

confidence: 99%

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

Tang

Cao

2021

Pharmaceutics

View full text Add to dashboard Cite

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

show abstract

Beyond Deterministic Models in Drug Discovery and Development

Cited by 22 publications

References 70 publications

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

Two heads are better than one: current landscape of integrating QSP and machine learning

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

Contact Info

Product

Resources

About