Disease system analysis between complexity and (over) simplification
Post, T.M.
Citation
Post, T. M. (2009, December 1). Disease system analysis between complexity and (over) simplification. TIPharma, Division of Pharmacology, Schering-Plough, Leiden/Amsterdam Center for Drug Research, Faculty of Science, Leiden University. Retrieved from
https://hdl.handle.net/1887/14477
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General introduction
Section 1
Scope and outline
Chapter 1
Chapter 1
12
Scope
The investigation in this thesis focused on establishing a theoretical framework for mechanism‐based disease progression modeling, with an emphasis on demonstrating proof‐
of‐concept in a chronic progressing disease. It was hypothesized that a combination of a mathematical representation of an underlying biological mechanism with actual physiological data reflecting disease progression and treatment effects can lead to comprehensive descriptions of chronically progressing diseases. In the end, this will result in models enabling the translation of data on short term pharmacological effects to predicted long‐term clinical outcomes.
An important prerequisite for the development of novel drugs is an ever increasing understanding of human biology. On the one hand an increased effort is required in drug discovery and development because of the complexity of biological systems and the much needed novel mechanisms of action. On the other hand pharmaceutical industry is continuously looking for ways to decrease the development time of new compounds.
Moreover, the development should be performed in a cost effective way. The investigation and understanding of diseases and drug treatment requires an increasing amount of resources. The ever increasing understanding of the complexity of the diseases and the connections between diseases, the desire to find ways to treat these diseases, the long duration of chronic diseases, the heterogeneous mechanisms of action and target sites, and the abundance of biomarker and clinical outcome data requires complex and comprehensive analysis techniques. Generally, treatment effects are analyzed using classical statistical methodologies. Albeit useful as they present the final confirmatory evidence on efficacy requested by regulatory agencies based on clinical endpoints, they inherently lack the ability to make use of the information‐rich time‐course profiles of biomarkers obtained during the earlier drug development cycles. Assumption‐rich models, e.g., non‐linear mixed‐effects models or population models, have enhanced power to separate noise or uncertainty from the pharmacological signal by the pooling of information‐rich data across trials, doses and mechanisms of action (4, 9, 10, 12).
Despite the abundance of dynamical biomarker data in various diseases, the field of pharmacokinetic‐pharmacodynamic and disease progression modeling has also only begun to increase the complexity of the models that are utilized in drug development and clinical practice. An important and interesting factor in the challenging task of quantitative drug development and clinical practice investigations is the ‘time factor’ related to disease
Scope and outline
13 progression in chronic disease and the time scales on which a treatment effect manifests itself in a biomarker response and in the clinical outcome.
The population pharmacokinetic‐pharmacodynamic (PK‐PD) and disease progression (DP) modeling approach is well established in drug development and has been recognized as a key factor in the improvement of drug development efficiency (1, 2, 10). It is a data analysis technique that quantitatively characterizes the interrelationship between the treatment (pharmacokinetics, pharmacodynamics) and the physiology of the disease (disease progression) over time. To date the application of complex models has been shown advantageous, going from empirical descriptive models to more complex mechanism‐based models. The pharmacokinetic‐pharmacodynamic and disease progression models aid in an effective integration of information from multiple sources and in the investigation of the effects of novel and established treatments based on underlying mechanistic pathways, ultimately leading to improved extrapolation and prediction of drug treatment effects.
Pharmaceutical companies, academia and regulatory agencies have initiated several projects to further increase and utilize the advantages of this model‐based approach (8). As an example, the FDA ‘critical path’ initiative document characterizes model‐based drug development (MBDD) as: ‘the development and application of pharmaco‐statistical models of drug efficacy and safety from preclinical and clinical data to improve drug development knowledge management and decision‐making’ (6, 9, 13). The FDA has developed initiatives to compose
‘model libraries’ for knowledge management and regulatory decision making (http://www.fda.gov/AboutFDA/CentersOffices/CDER/ucm167032.htm). This has the advantage of having an integrated information‐rich prior knowledge‐base on drugs and diseases available during drug development, review and registration. Based on these models hypotheses can be tested or generated during clinical trials, for instance to learn about or to validate the animal models that were used during initial development, to investigate exposure‐response relationships or dropout patterns (5, 15). In addition subgroups for which dose adjustment is needed can be identified, and anticipated treatment effects can be simulated based on early stage development. Furthermore, the most optimal clinical trial design can be investigated to study drug treatment effects or drug treatment combinations (3, 7, 9). Finally, the efficacy and safety balance can be weighed using a clinical utility function (11, 14). These factors combined support the most efficient and cost‐effective drug development.
Chapter 1
14
Outline
The thesis starts with an overview and discussion of pharmacokinetic‐pharmacodynamic and disease progression modeling concepts, with emphasis on going from pharmacodynamics to disease progression modeling. The second section starts by introducing the concept of disease system analysis (DSA) and presents an overview of disease progression signature profiles based on indirect response models (Chapter 3). Here the importance of the interplay and difference between time scales and levels in the physiology is stressed. Furthermore, the combination of data and other relevant information related to various levels of physiology and clinical outcome is advocated in order to advance the understanding of disease progression and drug treatment effects. It is shown that distinct time courses exist that reflect symptomatic and disease modifying effects. Chapter 4 introduces a method to evaluate model performance in model‐based investigations, which is a key matter when looking at long term predictions. It is shown that the performance of models established on large datasets and longitudinal data should be evaluated irrespective of the data density, while also taking into account missing data.
The third section focuses on the application of the DSA approach in postmenopausal osteoporosis. Chapter 5 presents an overview of the information that is available on bone physiology, disease and treatment. The application of modeling techniques is discussed and the possibility for a DSA approach is outlined. In Chapter 6 the proof of concept of a disease system for postmenopausal osteoporosis is discussed. This analysis showed an additional aspect to the modeling of disease progression by characterizing distinct symptomatic and disease modifying effects in different biomarkers. This analysis shows that more complex disease systems based on conceptual mathematical frameworks enable the characterization of a modular disease system that in the end could enhance drug development.
In Chapter 7 the investigations are reviewed and a focus is put on the application of the disease system for postmenopausal osteoporosis in the evaluation of novel – including theoretical – and established mechanisms of action.
References
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