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Co-authored-by: esqlabs GmbH <stephan.schaller@esqlabs.com>

.github/workflows/wordlist.txt

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 Subunits
 subchapters
 ReportingEngine
-subunits
+subunits
+nibr
+opensource
+xgx
+metrumresearchgroup
+mrgsolve
+pbpk
+qsp
+Türk’s
+FDA's
+Yun’s

SUMMARY.md

@@ -5,7 +5,13 @@
 * [How to Contribute](how-to-contribute.md)
 
 ## Mechanistic Modeling of Pharmacokinetics and Dynamics
-
+* Best Practices
+    * [Introduction](part-7/a-short-guide-to-pbpk-model-development.md)
+    * [Model Development](part-7/model-development.md)
+    * [Model Evaluation](part-7/model-evaluation.md)
+    * [Application Simulation](part-7/application-simulation.md)
+    * [Documentation](part-7/documentation.md)
+
 * Modeling Concepts
   * [PBPK Modeling - Systems Biology](part-1/modeling-concepts-pbpk-modeling-systems-biology.md)
   * [PK and PD Modeling](part-1/modeling-concepts-pk-and-pd-modeling.md)

part-7/a-short-guide-to-pbpk-model-development.md

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+# A short guide to PBPK model development, evaluation, simulations and documentation
+
+The versatile nature of physiologically based pharmacokinetic (PBPK) modeling facilitates many opportunities of application but at the same time also for different approaches in terms of execution. This inevitably introduces the questions on way of working and best practices. How should model development, including challenges addressed and assumptions made, be conducted and reported? How should analyses be performed at different stages in drug development to ensure robust results with confidence, reproducibility and traceability? To guide the users of the OSP-Suite we here present our view on best practices for PBPK modeling. The material is categorized under the sections Development, Evaluation, Application&Simulation and Documentation  including a repository of relevant literature to facilitate further reading on the topic.  
+
+![Guide to PBPK model development, evalution, simulations and documentation](../assets/images/part-7/a-short-guide-to-pbpk-model-development-1.png)
+
+To note, there are a number of existing review, overview, tutorial, and guidances available to the PBPK Community. This site is not intended to rewrite those materials but instead to serve as a landing page for individuals seeking to learn or to branch further into PBPK modeling. A sampling of existing learning content is listed below:
+
+[[123](../references.md#123)] - [[130](../references.md#130)]

part-7/application-simulation.md

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+# Application Simulation
+## Intended-use scenario-based applications:
+
+- DDI 
+    - Application Case Examples
+        - Case-scenario of an industry-application PBPK bottom-up modeling approach used to evaluate the DDI potential of acalabrutinib and its active metabolite, with CYP3A inhibitors and inducers [[149](../references.md#149)].
+    - Model Template Development
+        - Türk’s paper describes a comprehensive workflow of DDI module in PK-Sim and the Supplementary Materials to this manuscript were compiled as one comprehensive reference manual with transparent documentation of the model performance to support DDI investigations during drug development, labeling, and submission for regulatory approval of new drugs [[145](../references.md#145)].
+- Special Populations / Organ Impairment
+    - Pediatrics
+        - Yun’s paper determined the appropriateness of the virtual individual creating algorithm in PK-Sim® in predicting PK parameters and their variability in children by comparing a model output, clearance, to observed data. Identified the critical system specific input parameters within a pediatric PBPK model structure for estimating exposure in children via a sensitivity analysis [[147](../references.md#147)].
+        - A brief overview of the development of pediatric physiologically based pharmacokinetic (PPBPK) models, the challenges of uncertain systems information, and finally performance verification considering recent regulatory guidance [[138](../references.md#138)].
+    - Pregnancy 
+        - These manuscripts provide overview of pregnancy model in PK-Sim and  its major aspect of the model and physiology changes [[132](../references.md#132)]
+- Organ Impairment
+    - Reviews 
+        - PBPK predictions can help determine the need and timing of organ impairment study. It may be suitable for predicting the impact of RI on PK of drugs predominantly cleared by metabolism with varying contribution of renal clearance [[136](../references.md#136)]. 
+    - CKD 
+        - The renal diseases also affect drug metabolization by the liver. Tan et al. provides a comprehensive workflow used for investigation of pharmacokinetics on patients with CKD [[144](../references.md#144)].
+    - Liver 
+        - PBPK Modeling for prospective dose recommendations and efficacy/safety assessment in special populations (when consistent clinical data are lacking). Example for a PBPK model to predict the effect of moderate and severe hepatic impairment on the PK of alectinib to best inform clinical study design [[141](../references.md#141)].
+- Virtual Bioequivalence (VBE)
+    - Average bioequivalence studies have been required by the FDA and the EMA. These publications explore a workflow and discuss data requirements to run Virtual BE using PBPK [[139](../references.md#139)], [[140](../references.md#140)].
+- Regulatory Review
+    - This report reviews the use of PBPK in decision-making during regulatory review. The report also discusses the challenges encountered when PBPK modeling and simulation were used in these cases and recommends approaches to facilitating full utilization of this tool.It also summarize general schemes of PBPK simulation and propose procedures to obtain necessary data to construct PBPK models. In order to fully utilize PBPK in drug development and regulatory review, it is critical to adequately define mechanisms of drug disposition and understand general physiological perturbations related to diseases,age, and organ dysfunction [[148](../references.md#148)].
+    - This white Paper summarises the FDA's view how a framework for evidential criteria for PBPK models can be established. With that the FDA reached out to the scientific community to stimulate a discussion about this topic [[137](../references.md#137)].
+    - Overview on use of PBPK for submissions to the FDA. Discusses limitations and knowledge gaps in integration of PBPK to inform regulatory decision making, as well as the importance of scientific engagement with drug developers who intend to use this approach [[135](../references.md#135)].
+
+## Simulation (i.e. application) design / strategy considerations:
+- Population-level vs mean
+    - This case study for Caffeine shows that individual pharmacokinetic profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model informed precision dosing approaches in the future [[134](../references.md#134)].
+- Workflow Review 
+    - This review of several case studies provides is for a better understanding of the absorption, distribution, metabolism and excretion (ADME) workflow of a drug candidate, and the applications to increase efficiency, reduce the need for animal studies, and perhaps to replace clinical trials. The regulatory acceptance and industrial practices around PBPK modeling and simulation is also discussed  [[150](../references.md#150)].
+- Hypothesis generation
+    - The aim of this paper was to develop an analysis framework to investigate whether population modelling approach can be used to estimate PBPK model parameters from clinical PK data and establish the required criteria for such estimations [[131](../references.md#131)].	
+- Regulatory Confidence
+    - It is a perspective case of workshop entitled “Application of Physiologically-based Pharmacokinetic (PBPK) Modeling to Support Dose Selection” was hosted on March 10, 2014 by the US Food and Drug Administration (FDA) at its White Oak Campus in Silver Spring, MD. The workshop endeavored to (i) assess the current state of knowledge in the application of PBPK in regulatory decision-making, and (ii) share and discuss best practices in the use of PBPK modeling to inform dose selection in specific patient populations [[146](../references.md#146)]
+    - This white Paper summarises the FDA's view how a framework for evidential criteria for PBPK models can be established. With that the FDA reached out to the scientific community to stimulate a discussion about this topic [[137](../references.md#137)].
+- Case-based strategies for different application scenarios
+    - This work presents a systematic assessment of the current challenges to establishing confidence in PBPK models with respect to parameter estimation and model verification in each of the three major areas of PBPK application absorption prediction, exposure prediction in a target population, and DDI risk assessment during drug development [[142](../references.md#142)].

part-7/documentation.md

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+# Documentation
+
+While PBPK modelling is applied to inform decision making in the pharmaceutical industry to e.g. inform go/no-go decisions, formulation development, a dosing strategy in pediatrics or a DDI strategy one should keep in mind that PBPK modelling is a robust tool to support drug/chemical safety or toxicity risk assessment in general.
+
+Independent of whether PBPK modelling is used for internal decision making or for decisions by regulators, a minimum level of documentation is recommended to facilitate traceability and, later on, review by regulators. This minimum level of documentation should allow for establishing the link between data, data transformations and manipulation, final model/simulation code, and conclusions in order to facilitate traceability. The manuscript “Good practices in model-informed drug discovery and Development (MID3): Practice, Application and Documentation” [[169](../references.md#169)] provides an overview on different levels of documentation (memo, report), a suggestion for documentation of analysis plans and reports including high-level guidance for authors with respect to content and audience. Guidance on documentation of assumptions and assessment of assumptions during model development is also provided . A recent publication by Tan et al. focuses on PBPK model reporting for chemical risk assessment, expanding the already existing guidances for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals, providing a more general and harmonized framework for reporting of PBPK models [[170](../references.md#170)].
+
+## Documentation during the conduct of an analysis
+It is recommended to create a summary of the parameter identification steps describing all relevant steps and tested models leading to the current best model. It is recommended to capture the following information: 
+- Analysis dataset used for parameter identification 
+- Simulations included  
+- Rationale for the model / hypothesis tested  
+- Outcome/evaluation of the parameter identification step 
+- Parameters used in final model
+
+Most of the commercial PBPK software offer a built-in tracking of parameter identification steps such as e.g. the journal function in OSP.
+
+## Reporting (including analysis plans) of PBPK analysis
+FDA and EMA have both issued guidance documents for the industry on reporting of PBPK analysis outlining the recommended format and content of a report for regulatory submissions [[125](../references.md#125)], [[126](../references.md#126)]. The EMA guidance clarifies their expectation on qualifying a PBPK platform for the intended use. 
+
+A more detailed overview on sections to be included in such a report and guidance on expected content in each section is provided in Table 4 of a review on “Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective” by [[128](../references.md#128)]. The outlined sections are in line with the MID3 recommendations and the FDA guideline. Supplements to this publication include a template for analysis plans and reports. 
+
+A more recent publication [[170](../references.md#170)] focuses on PBPK model reporting for chemical risk assessment applications. It expands the existing guidances [[125](../references.md#125)], [[126](../references.md#126)], [[128](../references.md#128)] for pharmaceutical applications to support the assessment of the safety of environmental chemicals. The publication provides very detailed expectations towards the content of each section which go beyond the level of detail as provided by the existing guidelines. While Tan et al. address underlying modelling assumptions as part of the method section, Marshall et al. and Shebley et al. suggest a stand-alone section including documentation of assessing the impact of uncertainties in the assumptions taken.

part-7/model-development.md

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+# Model Development
+
+Prior to starting on model development, a requirements analysis should be conducted to assess and outline a model development strategy:
+-	the model purpose, i.e. its context of use which should include
+-	Organism / population characteristics (biometrics, genotype, disease state, …) and the 
+-	Experimental design
+-	the observed data  (e.g., QSA/PR, in-vitro, or in-vivo) available for model development
+-	Non-clinical & clinical data considerations, e.g., would it help the model development of a human PBPK model to develop an animal PBPK model (e.g. for FIM or if no IV data was collected in humans but in animals)?
+-	Individuals data vs population mean data: how will this impact model evaluation and qualification (variability and uncertainty assessments (Considered in Sections “3. Model Evaluation” and “4. Model Applications”)))
+-	and the model evaluation & qualification strategy (Section “Model Evaluation”)
+
+This requirements analysis should then be condensed into specifications (i.e., a strategy) for model development & qualification and documented within an analysis plan (see Figure1).
+
+![Model development](../assets/images/part-7/model-development-1.png) 
+
+Figure 1: Predict, Learn, and Confirm cycle in IVIVE-PBPK model development (adopted from [[168](../references.md#168)]).   
+
+Availability and quality of data for model development is the key element and has to be judged in the context of use (see “Useful Literature” below). 
+
+**As an example:** data quality, e.g. input parameters for compound PK properties such as fraction unbound in plasma (fup) may have been precisely measured or only predicted with some uncertainty through QSPR models. The latter might contain too much uncertainty and not be appropriate in the context of precision dosing estimates for a clinical trial, but might well be suitable for risk assessment in environmental toxicology.
+
+Thus, the key is to make yourself aware of the limitations of the available data considering:
+-  accuracy and suitability of data for PBPK model development. 
+-  in which systems the data have been collected 
+   -  fu or lipophilicity measures (partitioning media used, neutral or acid/base compound)
+   -  solubility (measured in water or biorelevant media)
+   -  dissolution profiles (what apparatus was used and at which pH values).
+
+What should not be neglected in the requirements analysis, is the evaluation of the information, data and structure which the PBPK framework and associated databases or “add-on modules” contain. Compound properties and context of use will require decisions and sound qualification on what to select from available options, e.g.: 
+- which partitioning calculation method to choose based on compound properties 
+- how to extend the model (with e.g. “add-on modules” found in modeling literature) if required to fulfill its purpose (i.e., customizing default model equations and structure to account for e.g. specific mechanisms of distribution or new organ compartments not covered by the default PBPK model structure).
+
+## Useful Literature
+- IVIVE / ADME [[159](../references.md#159)]
+  -  This best practice provides an overview of strategies for first in human prediction based on preclinical modelling. A Review of relevant scientific publications and case examples are provided as well.
+- Absorption [[160](../references.md#160)] 
+  - This review provides an overview of the determinants of intestinal absorption and first-pass elimination of drugs and focuses on the principles and applications of conventional in vitro–in vivo extrapolation (IVIVE) methods to predict Fabs, FG, and FH in humans.
+- Distribution [[161](../references.md#161)], [[162](../references.md#162)], [[163](../references.md#163)], [[164](../references.md#164)]
+  - These papers provide the state of art of mechanistic calculation of steady state tissue:plasma partition coefficients (Kt:p) of organic chemicals in mammalian species was developed.
+- Metabolism [[165](../references.md#165)]
+  - Benet and Sodhi proposed future pathways that should be investigated in terms of the relationship to experimentally measured clearance values, rather than model-dependent intrinsic clearance
+- Transporters & Excretion [[166](../references.md#166)], [[167](../references.md#167)]
+  - These data demonstrate the promise of using IVIVE of transporter-mediated drug clearance and highlight the importance of quantifying plasma membrane expression of transporters and utilizing cells that mimic the in vivo mechanism(s) of transport of drugs.
+
+## Further Reading
+- Data-driven model refinement and qualification (e.g. [[168](../references.md#168)], [[124](../references.md#124)] ) 
+  - The papers describe a definition of, “qualification”, a how-to work flow, regulatory perspective, and an case example for model refinement 
+
+- Expansion to a PBPK-QSP Model (Platform) (e.g. [[171](../references.md#171)], [[172](../references.md#172)]) 
+  - These papers highlight complex integrations of PBPK and PD/QSP for building disease platform level PBPK-QSP models, which include often efficacy-relevant target/receptor kinetics and occupancy, and deposition.