Pharmacodynamic phase model

This pharmacokinetic phase/pharmacodynamic phase model of dose-response can be described physiologically by considering each component of the phase as being separated by lipid membrane barriers that divide essentially aqueous environments. 

Among chemical-physics properties, lipophilicity is certainly a key parameter to understand and predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) of NCE furthermore, it contributes to model ligand-target interactions underlying the pharmacodynamic phase [15],... 

Exposure-response modeling can be an important component of a totality of evidence assessment of the risk of QTc prolongation. It can be evaluated in early-phase studies and as part of the conventiontil study of QTc prolongation, and may help inform further evaluation. There are many different types of models for the analysis of concentration-response data, including descriptive pharmacodynamic (PD) models and empirical models that link pharmacokinetic (PK) models (dose-concentration-response) with PD models. 

Phase I studies evaluate the pharmacokinetics and safety of the drug in a small number (tens) of healthy volunteers. Phase I studies are sometimes conducted in a small patient population (Proof of Concept studies) with a specific objective such as the validation of the relevance of preclinical models in man. The purpose of these studies may be the rapid elimination of potential failures from the pipeline, definition of biological markers for efficacy or toxicity, or demonstration of early evidence of efficacy. These studies have a potential go/no-go decision criteria such as safety, tolerability, bioavailability/PK, pharmacodynamics, and efficacy. Dosage forms used in Phase I or Proof of Concept studies must be developed with the objectives of the clinical study in mind. 

The measurement of pharmacodynamics (PD) parameters in phase 1 studies can be very informative. First of all it may help to define the starting dose for subsequent studies in patients. It may also help to build a PK/PD model, which can be used as a framework for further development. 

After optimization, scientists test the lead compounds in more sophisticated models including pharmacokinetics, pharmacodynamics, and toxicity. The optimal molecule selected from these assessments is then declared a new dmg candidate and moves on to the next phase (development). If a program is successful, it may take a total of 3-6 years from target selection and validation through lead generation, lead optimization, and preclinical evaluation in animals to candidate selection for a potential new medicine. 

In this case study a simulation strategy, based on a mechanistic PK/PD model, was developed to predict the outcome of the first time in man (FTIM) and proof of concept (POC) study of a new erythropoietin receptor agonist (ERA). A description of the erythropoiesis model, along with the procedures to scale the pharmacokinetics and pharmacodynamics based on preclinical in vivo and in vitro information is presented. The Phase I study design is described and finally the model-based predictions are shown and discussed. 

In general, multiple (up to 30-40) blood samples can be obtained per subject to measure dmg and metabolite concentrations as well as biomarkers in these phase I clinical trials. Furthermore, pharmacodynamic measurements can be included to get a first impression on the drug effect in humans, however, limited by the fact that healthy volunteers were studied and not patients. As strict inclusion and exclusion criteria are used, the demographic characteristics of the healthy volunteers do not provide sufficient spread to investigate the effect of intrinsic factors. Therefore, phase I trials provide very rich data to develop pharmacokinetic and pharmacodynamic models on biomarker, but cannot be used to develop models for efficacy, safety, influence of patient factors on PK and/or PD and disease progression. 

In 2008, a Phase lib study of bevirimat in patients failing HIV therapy due to drug resistance was completed successfully. The results of this study demonstrated that patients who have virus with the most commonly occurring amino acids at positions 369, 370, or 371 on Gag are much more likely to respond to bevirimat treatment. In contrast, those patients whose virus has polymorphisms (variants) at these positions are less likely to respond to the drug. Furthermore, pharmacokinetic/pharmacodynamic modelling demonstrated that a trough plasma concentration of greater than 20 pg/mL bevirimat is required for a robust response. 

Many types of modeling techniques are available in the discovery phase of drug development, from structure activity relationships (SAR) to physiology based pharmacokinetics (PBPK) and pharmacokinetics-/pharmacodynamics (PK/PD) to help choosing some of the lead compounds. Some tests that are carried out by discovery include techniques related to structure determination, metabolism, and permeability NMR, MS/MS, elemental analysis, PAMPA, CACO-2, and in vitro metabolic stability. Although they are important as a part of physicochemical molecular characterization under the biopharmaceutics umbrella, they will not be discussed here. The reader can find relevant information in numerous monographs [9,10]. 

Pharmacokinetic/pharmacodynamic modeling plays an important role during the NDA submission and review phase by integrating information from the pre-clinical and development phases. Existence of a well-defined PK/PD model furthermore enables the reviewer to perform PK/PD simulations for various scenarios. This ability helps the reviewer gain a deeper understanding of the compound and provides a quantitative basis for dose selection. Thus, PK/PD modeling can facilitate the NDA review process and help resolve regulatory issues. ... 

Figure 8.8 Flow diagram for involvement of pharmacokinetic and pharmacodynamic mode/computer-generated feedback into the iterative process of drug discovery from medicinal chemistry to the decision to enter phase II trials. This is not a comprehensive flow diagram for all aspects of drug discovery - it is restricted to the components of the process discussed in this chapter. This flow diagram emphasizes efficient involvement of in vitro and in vivo experimental science and computer modeling, in review of data obtained in phase I studies, in the decisions related to selection of the best compound for patient studies...

A further modification of this model is one presented by Kerbusch, Milligan, and Karlsson (2003) in an population pharmacodynamic analysis of saliva flow rate after administration of the M3-muscarnic antagonist, darifenacin. Data were pooled from Phase 1 studies from healthy volunteers and a Phase 2 study from subjects with overactive bladder disease. Also, different formulations were used in many of the studies. Hence, the data set was quite heterogeneous and it was anticipated that the residual error might not be consistent across individuals. Residual error was modeled as... 

Pharmacokinetic-pharmacodynamic models are becoming increasingly complex through the incorporation of covariate information, effect mechanisms, and lower quantification limits of assays which reveal compartments not previously known to exist. With sparse data collected during Phase 3 trials it may not be possible to develop and support such complex models because of... 

Bonate, P.L. Assessment of QTc interval prolongation in a Phase I study using Monte Carlo simulation. In Simulation for designing clinical trials A pharmacokinetic-pharmacodynamic modeling perspective. (Kimko, H.C. and Duffull, S., Eds.). New York, 2003... 

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