Review Article | Volume: 13, issue: 4, April, 2023

Application of in silico methods in clinical research and development of drugs and their formulation: A scoping review

Luciana Ferreira Mattos Colli Lúcio Mendes Cabral Guacira Correa Matos Carlos Rangel Rodrigues Valeria Pereira de Sousa   

Open Access   

Published:  Mar 28, 2023

DOI: 10.7324/JAPS.2023.87792
Abstract

The drug regularization process involves many steps that are complex and time-consuming. The demand for new drugs has prompted researchers and regulatory authorities to search for predictive methods that can streamline the development process. Current studies point to innovative computational techniques in a drug’s study phases. This study aims to carry out a scoping review of research involving the application of computational methods and in silico studies in the clinical research and development of new drugs. A scoping review was conducted according to the eferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Online databases from 2001 to 2021 in English were used and the trial registration was 10.17605/OSF.IO/USXCM. The development of protocols and the application of a computational method for researching new drugs and their formulation, published in a peer-reviewed journal, were included. The data extraction and analysis were performed by two independent reviewers. In this study, 312 articles were retrieved, of which 6 were duplicates. After the title was read, only 101 remained for analysis. After the abstracts were read, 34 papers were considered for the scoping review. The use of in silico methodologies has been expanding in terms of research into the development of new drugs and the improvement of existing products.


Keyword:     Drug discovery in silico clinical trial computational methods drug development vir


Citation:

Colli LFM, Cabral LM, Matos GC, Rodrigues CR, de Sousa VP. Application of in silico methods in clinical research and development of drugs and their formulation: A scoping review. J Appl Pharm Sci, 2023; 13(04):001–010. https://doi.org/10.7324/JAPS.2023.87792

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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INTRODUCTION

The efficacy and safety of a drug are basic concepts of health surveillance adopted by regulatory agencies around the world (ANMAT, 2022; ANVISA, 2021; European Medicine Agency (EMA), 2019; FDA, 2018; PMDA, 2022). One of the main tools for drug regulation is the clinical trial, which, for new drugs, can take up to 12 years or more and cost millions of dollars (Berndt et al., 2015; Dimasi et al., 1995; Jensen, 1987). To circumvent such factors, over the years, major technological advances have been made, and new methodologies have been developed to streamline the process of evaluating a new molecular entity (Ji et al., 2017; Kar and Leszczynski, 2017).

The clinical trial protocols aimed at registering a drug were standardized with the Common Technical Document, a publication of the International Council of Harmonization (ICH), in which guidelines for the quality, safety, and efficacy of drugs were postulated. In the safety guide (M4S (R2)), the ICH prescribes the pharmacological evaluation of the drug, the pharmacodynamic (PD) study, and the interaction with other drugs. In addition, assessments of absorption, distribution, metabolism, excretion, and toxicity are applied (ICH, 2004).

In line with international practices, the Food and Drug Administration (FDA) divides clinical trials for drug registration into interventional and observational studies, the first being more common and the second obtained through researchers’ observation of outcomes after the use of a particular drug (FDA, 2019).

The structure of clinical studies involving new drugs is agreed upon worldwide as having four phases. The application of in silico studies, especially the well-known physiologically based pharmacokinetic (PBPK) studies that now already have guides published by the FDA and EMA, can be a strategy to compose the regulatory dossier, shortening the time of its elaboration (EMA, 2016; FDA, 2016).

The discussion that predominates in the area is the development of a set of strategies to shorten the long years of research. One possibility to speed up clinical trials is the application of in silico studies, with the use of digital resources, aiming to assess the effect that a particular drug can have on the human body (Clermont et al., 2004; Mancini et al., 2018; Pappalardo et al., 2019; Sinisi et al., 2020).

This scoping review aims to provide an overview of the specialized scientific literature on the use of digital technologies in clinical trials, with the application of in silico trials in the evaluation of new drugs, and the improvement of already regularized drugs and their formulations, to anticipate events in a traditional in vivo clinical trial involving humans.


METHODS

A scoping review was carried out to identify the conditions for the application of in silico studies in the current context of clinical research with new drugs. A request was made to register the research in the Open Science Framework (OSF) with the number 10.17605/OSF.IO/USXCM.

The research was conducted in March 2022, using the databases Latin American and Caribbean Health Sciences Literature (Lilacs), National Library of Medicine (PubMed), MEDLINE, Web of Science, and Scopus, in English. The report of the present scoping review was prepared by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist (Page and Moher, 2018; Peter et al., 2020, 2022). Studies that applied in silico methods to evaluate new or already registered drugs, improvements in formulation, evaluation of drug interaction, and pharmacometrics were considered. The specific keywords/descriptors that are Health Science Descriptors addressed drug discovery, in silico clinical trials, computational methods, drug development, and virtual patients. Publications in English between 2001 and 2021 were adopted.

After the selected articles were read, a form was filled out with collected data, which were compared in an infographic. Data analysis sought (a) the virtual and in vivo patient; (b) the protocols adopted in the studies; (c) a comparison with a traditional clinical study, in cases where it was observed; and (d) the observed outcomes.


RESULTS

The literature search strategy took place in the MEDLINE/PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) and Lilacs databases; the terms used in the search were ((in silico) AND (clinicaltrials) AND (drugtrials)); ((computationalmethods) AND (drugdevelopment)); ((computationalmethods) AND (drugdiscovery)); ((model-informeddrugdiscoveryanddevelopment)); and ((Virtual PhysiologicalHuman) and (drugdevelopment)), with a total of 312 articles retrieved. Of these, six were duplicates; therefore, they were disregarded.

The first phase of the study was carried out by reading the titles and abstracts of the articles. Those that were within the scope and met the inclusion criteria proceeded to the second stage, in which the text was read in full. Two researchers performed the complete reading of 70 articles. In total, 34 articles were considered for the scoping review and 36 were eliminated because they used multiple methods or tools or their methodology did not specify the software used. The selection flowchart can be seen in Figure 1.

PKs and PDs simulation: PK/PB using software

GastroPlus™

Simulation models are increasingly used in drug development studies and formulation improvement. Their application seeks to speed up the process and guide the conduct in the design of a new drug, with the GastroPlus™ software being directed to this. The development of a drug has complex factors that are difficult to adjust, such as physical-chemical, physiological, and formulation factors. It is necessary to employ tools to support the process. In the scoping review, six studies were identified as involving the use of the GastroPlus™ PK simulator. The results of the scoping review with GastroPlus™ are summarized in Table 1.

In approaches to PK studies, the study conducted by Jereb et al. (2021) evaluated delayed-release tablet pantoprazole compared to dolutegravir and its impact on the patient’s gastrointestinal tract after a meal and in the fasted state. This study used virtual models, with pantoprazole performing better than dolutegravir in terms of bioavailability.

In the application of the method in the study of formulations in the comparison of different formulations, Kato et al. (2020) evaluated three formulations, A, B, and C, and their PK in oral use. They noted differences between the developed batches, including those that were not bioequivalent. In a similar objective, the study by Xia et al. (2013) used in silico techniques to evaluate the PK of developed formulations and the effect of feeding. In this study, the drug in the experimental phase NVS123, of basic character and with pH-dependent solubility, was evaluated.

The occurrence of changes in gastric pH is a biopharmaceutical event that can impact the bioavailability of several drugs. The study by Samant et al. (2018) evaluated the pH and its consequences on the absorption of ribociclib, finding that PK had no impact on the elevation of gastric pH. Similarly, this occurred in the evaluation of alectinib (Parrott et al., 2016).

Regarding the evaluation of formulations, GastroPlus™ proved to be a possibility in a generic candidate drug study [Biopharmaceutics Classification System (BCS) class 2] compared to the reference. Additionally, a clinical study and dissolution test were conducted. An additional concern was assessing the impact of food and fasting, with the tests supporting the construction of the regulatory dossier (EMA, 2016; FDA, 2016; Rebeka et al., 2019).

NONMEM®

NONMEM® is an acronym that stands for “NON-linear Mixed-Effects Modeling,” which is a software developed in the early 1980s, with an application in in silico studies involving the PKs of several drugs. The results of the scoping review with NONMEM® are summarized in Table 2.

In cases of patient exposure, the work by Li et al. (2015)used abiraterone and nilotinib to determine mock PK assays. The parameters adopted for the PK study were obtained from the mean and standard deviation of several published studies (Ryan et al., 2010; Tanaka et al., 2010; Zytiga, 2013 apud Li et al., 2015). The simulation is applied to evaluate possible results in a clinical trial in different dosing regimens looking at new treatments compared to methotrexate, with the possibility of understanding the endpoints for rheumatoid arthritis (RA) trials and clarifying confounding factors; the method was also applied with fesoterodine (Cardozo et al., 2010; Ma et al., 2014).

Figure 1. Flowchart for selecting articles for the scoping review, using the PRISMA methodology.

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Table 1. Scoping review studies involving PK simulators using GastroPlus™ software.

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Table 2. Scoping review studies involving PK simulators using NONMEM® software.

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In an evaluation of the sublingual route, the response to the dose of asenapine in patients with schizophrenia was characterized. The analysis enabled an understanding of the results of six placebo-controlled trials in which responses and dropout rates varied. Although the simulations indicated that the post hoc probability of success of the performed trials was low to moderate, these analyses demonstrated that asenapine doses of 5 and 10 mg twice daily have similar efficacy (Friberg et al., 2009).

Additionally, one study evaluated another route of administration, testing inhaled glucocorticoids in the work by Nathan et al. (2008) as a first-line therapy in asthma. The study sought to identify the optimal timing of dosing using two surrogate markers of glucocorticoid action. A previously published study (Mollmann et al., 2001 apud Nathan et al., 2008) on the PK and PD (blood cortisol and lymphocyte suppression) of the glucocorticoids budesonide and fluticasone propionate was reanalyzed using a population PK approach allowing established dosage. This can be applied in pediatric dose-setting cases, such as carvedilol for children (Albers et al., 2008) (Table 2).

Clinical trial simulations and PK/PD models were conducted to recommend a study design to test the dose of the compound SC-75416, a selective inhibitor of cyclooxygenase-2, in pain relief compared to 400 mg of ibuprofen in a model of pain after oral surgery. Study results confirmed the hypothesis that 360 mg of SC-75416 achieved superior pain relief compared to 400 mg of ibuprofen and demonstrated the predictive performance of PKPD models (Kowalski et al., 2008). In a biomarker approach, the PD of another MEDI-546 test compound, a monoclonal antibody, was characterized by modeling and simulation (Wang et al., 2013).

Application in therapeutic drug monitoring can also be performed using in silico methods. Studies with mycophenolate mofetil (MMF) in a fixed-dose regimen and another regimen of a controlled concentration of mycophenolic acid exposure were developed. Estimates for oral clearance of MMF were used to calculate values in the area under the curve (Van Hest et al., 2005).

Simcyp™ simulator in drug interaction

Bioequivalence and bioavailability studies can also be conducted with the Simcyp™ Simulator software, which is the PBPK model platform for determining human dosing, optimizing the design of clinical studies, evaluating new drug formulations, defining the dose in untested populations, and performing virtual analyses of bioequivalence and drug interactions (Certara, 2022). The data of the scoping review with Simcyp™ are summarized in Figure 2.

The Simcyp™ ADME Simulator can also be a database for simulation modeling of oral absorption, tissue distribution, drug metabolism, and excretion, and drug development studies in certain populations predicting the extent of action and drug–drug interaction (Jamei et al., 2009).

The drug interaction studies were observed using Simcyp™. In one of them, models of interaction between the target drug nemiralisib and itraconazole were used; additionally, midazolam and clarithromycin were evaluated (Patel et al., 2020; Yu et al., 2017).

Figure 2. Infographic illustrating studies mapped to Simcyp™, with key data, drugs, and authors.

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Another study evaluated enzyme inhibitors, with concomitant application of in vitro, in silico, and in vivo methods. The study determined whether repaglinide had an inhibitory effect on pioglitazone metabolism. The authors observed a discrepancy in the result between the experiments (Xiao et al., 2015) (Fig. 2).

In a different modality of study, a nanoscale formulation was evaluated by Litou et al. (2019). In the study design, in vitro results were coupled to a PBPK model. The evaluation was with aprepitant (EMEND), which is indicated for nausea and vomiting, especially during chemotherapy. In cases involving nanomeric formulations, it is necessary to apply innovative tools to understand their in vivo performance and guide the regulatory process (Fig. 2).

The approach used with perampanel was structured with data from in vitro studies and a phase I trial (Patsalos, 2015). The peak plasma concentration of perampanel (Cmax) and time to Cmax showed no apparent differences when perampanel was administered alone versus with ketoconazole (Gidal et al., 2017).

Monte Carlo simulation

The Monte Carlo simulation or Monte Carlo method, which is a branch of experimental or applied mathematics involving random numbers, has applications in several areas of knowledge, such as mathematics, physics, economics, and even medical sciences (Carvalho, 2017).

The method or model is essentially characterized by the use of software that, with simulation platforms, expands the sample size of a study and provides simulations for the outcome of treatment or, more precisely, for a particular therapeutic target, considering different situations, such as changes in a dose or frequency of drug administration (Federico et al., 2017).

In the study by Zhang et al. (2011), Monte Carlo simulation was applied to generate hypothetical cohorts with 7,000 patients characterizing the so-called discrete event simulation (DES) (Fig. 3). In the research, we investigated the effectiveness of rivaroxaban in preventing stroke in patients with atrial fibrillation. Hypothetical patient cohorts were generated using data from ROCKET AF (Patel et al., 2011) (FDA registration code NCT00403767) and two other observational studies (Amin et al., 2017; Laliberté et al., 2014) and Xantus (Camm et al., 2016). The results confirmed that rivaroxaban was noninferior to warfarin for the prevention of stroke/systematic embolism, with no significant risk of major bleeding in atrial fibrillation in large populations. This was similar to the results of ROCKET AF.

In a process that involved applying data from previously performed clinical trials, Najafzadeh et al. (2018) used data from the RE-LY study (2009), as well as cohorts of equal size with covariate distributions identical to the study of Graham et al. (2015). Cohort simulations were performed using the Monte Carlo method and compared to a randomized clinical trial. Another study that used Monte Carlo simulations to interpret data from a randomized clinical trial was performed by Cuadros et al. (2014); in this study, study simulations involving male circumcision in trials with valaciclovir for the suppression of herpes simplex were performed (Fig. 3).

Opioids are subject to evaluation, due mainly to their application in pain, to evaluate long-acting opioids in patients with nonmalignant chronic pain classified as moderate to severe. Neil et al. (2013) developed a Monte Carlo simulation. Long-term opioid efficacy and adverse events were obtained from clinical trials with tapentadol ER versus oxycodone CR; other data were taken from the literature. The use of tapentadol proved to be superior in effectiveness and cost-effectiveness, demonstrating the successful use of Monte Carlo in a pharmacoeconomics study.

Another study on chronic pain was carried out by Murthy et al. (2007) with once-daily extended-release tramadol (tramadol ER) approved in the US for moderate to moderately severe chronic pain in adults. Monte Carlo simulation was performed to assess switching in patients who received immediate-release tramadol by ER tramadol. PK analyses showed that switching from a total daily dose of tramadol IR 200 or 300 mg to tramadol ER 200 and 300 mg once daily is equivalent.

STELLA®

STELLA® software is a dynamic systems simulation that helps one understand complex correlations within a system of data relationships. It is used in modeling, providing tools to convert numerical models into formulation evaluation (Naimi et al., 2012).

Three studies of Shono were found to use STELLA® software: one from 2011, another from 2010, and a third from 2009. The first study, by Shono et al. (2011), developed an in silico PBPK for poorly soluble nelfinavir mesylate in water and of weakly basic pH-generating plasma profiles and of coupling dissolution results and precipitation estimates with gastrointestinal parameters.

The second study, by Shono et al. (2010), coupled biorelevant dissolution test results with in silico simulation technology to predict the in vivo oral absorption of aprepitant formulations with micronized and nanosized particles in the preprandial and postprandial states.

Figure 3. Infographic illustrating the studies mapped to Monte Carlo simulation with key data, drugs, and authors.

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The third and oldest study, by Shono et al. (2009), determined the rate of intestinal absorption of poorly soluble drugs and dissolution in the gastrointestinal tract. In this study, in vitro dissolution tests using biorelevant media coupled with PBPK in silico were applied to predict the effects of food on the absorption of a poorly soluble drug, celecoxib, from celecoxib 200 mg capsules.


DISCUSSION

This study described and characterized the types of in silico methods for research involving new drugs and the improvement of existing ones. Simulation models have advanced in recent years, and have been shown to be tools increasingly used in drug development and formulation studies. Such development and innovation motivated several researchers to evaluate new software for conducting clinical trials in their work.

The most applied and tested in silico studies by the scientific community within the parameters researched pointed to the use of software such as GastroPlus™, NONMEM®, Simcyp™, Monte Carlo, and STELLA®.

Of the tests evaluated, the GastroPlus™ software was continuously employed in human PK and PD assessments of several different drug types and formulations. The in silico method was also able to discriminate between bioequivalent and nonbioequivalent batches. With the software, it was also possible to perform in silico clinical evaluations of the influence of changes in gastric pH and food intake on the PK of a drug.

In terms of evaluating new compounds, two studies studied new drugs. These were new formulation clinical investigations and, most importantly, highlighted a practical application of PBPK modeling in solving problems involving undesirable food effects on weakly basic compounds based on in vitro/in vivo data. The various studies retrieved in the proposed search demonstrated that the in silico method using GastroPlus™ is efficient in evaluating different drug formulations, changes in the drug’s crystalline arrangement, or even the use of known and regularized drugs at different stages of the digestion process.

Other publications pointed out the use of NONMEM® software. The searches retrieved ten scientific articles evaluating several drugs and the application of the software aimed to establish pediatric doses, inhaled drugs, drug interactions, and pharmacometry.

Some studies clearly did dosage reviews or sought to determine new dosages in different audiences.

The development of innovative drugs is not the only application of in silico methods but may well lend itself to developing better evaluations of already regulated drugs, which have a variable PK profile, and also the impact that food can have.

The remaining studies found were conducted using the Simcyp™ Simulator software to determine the human dosage of various compounds. They also evaluated the drug–drug interaction and PK of several drugs, as well as the behavior of different formulations.

Of the articles found in the research, three pointed to the use of the Monte Carlo method, which was applied to expand the sample of volunteers and create simulations of responses.

To evaluate the drugs nelfinavir, celecoxib, and aprepitant, studies were found that used the STELLA® software to evaluate the dissolution in the moments before and after the meal.


CONCLUSION

The in silico studies observed in this work proved its applicability in the research of new drugs, as well as in the improvement of the evaluated formulations, with the approaches of PK evaluation and drug–drug interaction evaluation.

The evaluated studies have differences in terms of the drug evaluated, the number of simulated patients, the protocol adopted, and the in silico technology addressed. For this reason, comparing results is difficult. However, it is possible to observe the application of software and the evaluation of drugs in different simulated approaches.

The application of in silico methods to evaluate a drug or medication intensified in the last decade and its use has been expanding. This meets the need for more agile studies with lower costs in the development of new drugs.


ACKNOWLEDGMENTS

The authors would like to acknowledge the staff members of Universidade Federal do Rio de Janeiro, the Faculty of Pharmacy, for their support of this work.


AUTHOR CONTRIBUTIONS

Concept and design, acquisition of data, or analysis and interpretation of data were carried by Colli and Cabral. Drafting the article and revising it critically for important intellectual contente were carried out by Matos, Rodrigues and Sousa.


FINANCIAL SUPPORT

There is no funding to report.


CONFLICTS OF INTEREST

The authors report no financial or any other conflicts of interest in this work.


ETHICAL APPROVALS

This study does not involve experiments on animals or human subjects.


DATA AVAILABILITY

All data generated and analyzed are included within this research article and in the searchRxiv platform in the link https://doi.org/10.1079/searchRxiv.2022.00006.


PUBLISHER’S NOTE

This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.


REFERENCES

 Albers S, Meibohm B, Mir TS, Laer S. Population pharmacokinetics and dose simulation of carvedilol in pediatric patients with congestive heart failure. Br J Clin Pharmacol, 2008; 65(4):511–22. CrossRef

 Amin A, Keshishian A, Trocio J, Dina O, Le H, Rosenblatt L, Liu X, Mardekian J, Zhang Q, Baser O, Vo L. Risk of stroke/systemic embolism, major bleeding and associated costs in non-valvular atrial fibrillation patients who initiated apixaban, dabigatran or rivaroxaban compared with warfarin in the United States Medicare population. Curr Med Res Opin, 2017; 33(9):1595–604.

 ANMAT. Nuevos aranceles para mantenimiento de registros de productos médicos y especialidades medicinales [Online]. Available via https://www.argentina.gob.ar/noticias/nuevos-aranceles-para-mantenimiento-de-registros-de-productos-medicos-y-especialidades (Accessed 13 February 2022). CrossRef

 ANVISA. Protocolo de segurança e eficácia de medicamentos inovadores. 2021 [Online]. Available via https://www.gov.br/anvisa/pt-br/setorregulado/regularizacao/medicamentos/informes/medicamentos-sinteticos/protocolo-de-seguranca-e-eficacia-de-medicamentos-inovadores (Accessed 13 February 2022).

 Berndt ER, Nass D, Kleinrock M, Aitken M. Decline in economic returns from new drugs raises questions about sustaining innovations. Health Affairs, 2015; 34(2):245–52. CrossRef

 Camm AJ, Amarenco P, Haas S, Hess S, Kirchhof, Kuhls S, van Eickels M, Turpie AG, XANTUS Investigators. XANTUS: a real-world, prospective, observational study of patients treated with rivaroxaban for stroke prevention in atrial fibrillation. Eur Heart J, 2016; 37(14):1145–53. CrossRef

 Cardozo L, Khullar V, El-Tahtawy A, Guan Z, Malhotra B, Staskin D. Modeling dose-response relationships of the effects of fesoterodine in patients with overactive bladder. BMC Urol, 2010; 10(1):1–11. CrossRef

 Carvalho ARD. Método Monte Carlos e suas aplicações. 122 f. Dissertação (Mestrado em Matemática), Universidade Federal de Roraima, Boa Vista, Brazil, 2017.

 Certara. SIMCYP PBPK Simulator. Versão 21. Phoenix: Certara, 2022 [Online]. Available via https://www.certara.com/software/simcyp-pbpk/ (Accessed 4 April 2022).

 Clermont G, Bartels SJ, Kumar R, Constantine G, Vodovotz Y, Chow C. In silico design of clinical trials: a method coming of age. Crit Care Med, 2004; 32(10):2061–70. CrossRef

 Cuadros DF, Abu-Raddad LJ, Awad SF, García-Ramos G. Use of agent-based simulations to design and interpret HIV clinical trials. Comput Biol Med, 2014; 50:1–8. CrossRef

 Dimasi JA, Hansen RW, Grabwski HC, Lasagna L. Research and development costs for new drugs by therapeutic category. Pharm Econ, 1995; 7(2):152–69. CrossRef

 EMA. Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation, 2016 [Online]. Available via http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2016/07/WC500211315.pdf (Accessed 27 May 2022).

 EMA. Research and development [Online]. Available via https://www.ema.europa.eu/en/human-regulatory/research-development (Accessed 13 February 2022).

 FDA. Guidance for industry: physiologically based pharmacokinetic analyses—format and content, 2016 [Online]. Available via https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM531207.pdf (Accessed 27 May 2022).

 FDA. Learn about clinical studies. 2019. [Online]. Available via https://clinicaltrials.gov/ct2/about-studies/learn (Accessed 13 February 2022).

 FDA. The drug development process. Step 3: Clinical research. FDA, 2018. [Online]. Available via https://www.fda.gov/patients/drug-development-process/step-3-clinical-research (Accessed 13 February 2022).

 Federico MP, Sakata RAP, Pinto PFC, Furtado GHC. Noções sobre parâmetros farmacocinéticos/farmacodinâmicos e sua utilização na prática médica. Rev Soc Brasil Clín Méd, 2017; 15(3):201–5.

 Fransson M, Gréen H. Comparison of two types of population pharmacokinetic model structures of paclitaxel. Eur J Pharm Sci, 2008; 33(2):128–37. CrossRef

 Friberg LE, De Greef R, Kerbusch T, Karlsson MO. Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Clin Pharm Ther, 2009; 86(1):84–91. CrossRef

 Gidal BE, Maganti R, Laurenza A, Yang H, Verbel DA, Schuck E, Ferry J. Effect of enzyme inhibition on perampanel pharmacokinetics: why study design matters. Epilepsy Res, 2017; 134:41–8. CrossRef

 Graham DJ, Reichman ME, Wernecke M, Zhang R, Southworth MR, Levenson M, ... Kelman JA. Cardiovascular, bleeding, and mortality risks in elderly medicare patients treated with dabigatran or warfarin for nonvalvular atrial fibrillation. Circulation, 2015; 131(2):157–64. CrossRef

 ICH. M4 Organisation of the common technical document for the registration of pharmaceuticals for human use guidance for industry. ICH, Geneva, Switzerland, 2004.

 Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp® population-based ADME simulator. Expert Opin Drug Metab Toxicol, 2009; 5(2):211–23. CrossRef

 Jensen EJ. Research expenditures and the discovery of new drugs. J Indus Econ, 1987; 36:83–95. CrossRef

 Jereb R, Opara J, Bajc A, Petek B. Evaluating the impact of physiological properties of the gastrointestinal tract on drug in vivo performance using physiologically based biopharmaceutics modeling and virtual clinical trials. J Pharm Sci, 2021; 110(8):3069–81. CrossRef

 Ji Z, Yan K, Li W, Hu H, Zhu X. Mathematical and computational modeling in complex biological systems. BioMed Res Int, 2017; 2017:1–16. CrossRef

 Kar S, Leszczynski J. Recent advances of computational modeling for predicting drug metabolism: a perspective. Curr Drug Metab, 2017; 18(12):1106–22. CrossRef

 Kato T, Nakagawa H, Mikkaichi T, Miyano T, Matsumoto Y, Ando S. Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches. Eur J Pharm Sci, 2020; 151:45–52. CrossRef

 Kowalski KG, Olson S, Remmers AE, Hutmacher MM. Modeling and simulation to support dose selection and clinical development of SC-75416, a selective COX-2 inhibitor for the treatment of acute and chronic pain. Clin Pharmacol Ther, 2008; 83(6):857–66. CrossRef

 Laliberté F, Cloutier M, Nelson WW, Coleman CI, Pilon D, Olson. Real-world comparative effectiveness and safety of rivaroxaban and warfarin in nonvalvular atrial fibrillation patients. Curr Med Res Opin, 2014; 30(7):1317–25. CrossRef

 Li CH, Sherer EA, Lewis LD, Bies RR. Clinical trial simulation to evaluate population pharmacokinetics and food effect: capturing abiraterone and nilotinib exposures. J Clin Pharmacol, 2015; 55(5):556–62. CrossRef

 Litou C, Patel N, Turner DB, Kostewicz E, Kuentz M, Box KJ, Dressman J. Combining biorelevant in vitro and in silico tools to simulate and better understand the in vivo performance of a nano-sized formulation of aprepitant in the fasted and fed states. Eur J Pharm Sci, 2019; 138:105031.

 Ma L, Zhao L, Xu Y, Yim S, Doddapaneni S, Sahajwalla CG, Wang Y, Ji P. Clinical end point sensitivity in rheumatoid arthritis: modeling and simulation. J Pharmacokinet Pharmacodyn, 2014; 41(5):537–43. CrossRef

 Mancini T, Mari F, Massini A, Melatti I, Salvo I, Sinisi S, Tronci E, Ehrig R, Röblitz S, Leeners B. Computing personalised treatments through in silico clinical trials. A case study on down regulation in assisted reproduction. Intell Artif, 2018; 2271:1–16. CrossRef

 Mollmann H, Wagner M, Krishnaswami S, Dimova H, Tang Y, Falcoz C, Daley-Yates PT, Krieg M, Stöckmann R, Barth J, Lawlor C, Möllmann AC, Derendorf H, Hochhaus G. Single-dose and steady-state pharmacokinetic and pharmacodynamic evaluation of therapeutically clinically equivalent doses of inhaled fluticasone propionate and budesonide, given as Diskus or Turbo haler dry-powder inhalers to healthy subjects. J Clin Pharmacol, 2001; 52(5):487–95. CrossRef

 Murthy BP, Skee DM, Danyluk AP, Brett V, Vorsanger GJ, Moskovitz BL. Pharmacokinetic model and simulations of dose conversion from immediate-to extended-release tramadol. Curr Med Res Opin, 2007; 23(2):275–84. CrossRef

 Nathan RA, Berger W, Yang W, Cheema A, Silvey M, Wu W, Philpot E. Effect of once-daily fluticasone furoate nasal spray on nasal symptoms in adults and adolescents with perennial allergic rhinitis. Ann Allerg Asth Immunol, 2008; 100(5):497–505. CrossRef

 Naimi B, Voinov A. Stella R: a software to translate Stella models into R open-source environment. Environ Modell Software, 2012; 38:117–8. CrossRef

 Najafzadeh M, Schneeweiss S, Choudhry NK, Wang SV, Gagne JJ. Simulation for predicting effectiveness and safety of new cardiovascular drugs in routine care populations. Clin Pharmacol Ther, 2018; 104(5):1008–15. CrossRef

 Neil N, Merchant S, Provenzano D, Ogden K, Mody SH. Clinical simulation model of long-acting opioids for treatment of chronic non-cancer pain in the United States. J Med Econ, 2013; 16(2):307–17. CrossRef

 Page MJ, Moher D. Extensions in development: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Syst Rev, 2018; 6(1):1–14. CrossRef

 Pappalardo F, Russo G, Tshinanu FM, Viceconti M. In silico clinical trials: concepts and early adoptions. Brief Bioinform, 2019; 20(5):1699–708. CrossRef

 Parrott NJ, Yu LJ, Takano R, Nakamura M, Morcos PN. Physiologically based absorption modeling to explore the impact of food and gastric pH changes on the pharmacokinetics of alectinib. AAPS J, 2016; 18(6):1464–74. CrossRef

 Patel A, Wilson R, Harrell AW, Taskar KS, Taylor M, Tracey H, Riddell K, Georgiou A, Cahn AP, Marotti M, Hessel EM. Drug interactions for low-dose inhaled nemiralisib: a case study integrating modeling, in vitro, and clinical investigations. Drug Metab Dispos, 2020; 48(4):307–16. CrossRef

 Patel MR, Mahaffey KW, Garg J, Pan G, Singer De, Hacke W. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med, 2011; 365(10):883–91. CrossRef

 Patsalos PN. The clinical pharmacology profile of the new antiepileptic drug perampanel: a novel noncompetitive AMPA receptor antagonist. Epilepsia, 2015; 56(1):12–27. CrossRef

 Peters MD, Godfrey C, McInerney P, Khalil H, Larsen P, Marnie C, Pollock D, Tricco AC, Munn Z. Best practice guidance and reporting items for the development of scoping review protocols. JBI Evid Synth, 2022; 20(4), 953–968. CrossRef

 Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: Scoping reviews. In: Aromataris E, Munn Z (Eds.). JBI Manual for evidence synthesis. JBI, 2020. [Online] Available via https://synthesismanual.jbi.global (Accessed 20 February 2022). CrossRef

 PMDA. History [Online]. Available via https://www.pmda.go.jp/english/about-pmda/outline/0002.html (Accessed 13 February 2022).

 Rebeka J, Jerneja O, Igor L, Boštjan P, Aleksander B, Simon Ž, Albin K. PBPK absorption modeling of food effect and bioequivalence in Fed State for two formulations with crystalline and amorphous forms of BCS 2 class drug in generic drug development. AAPS PharmSciTech, 2019; 20(2):1–10. CrossRef

 Ryan CJ, Smith MR, Fong L, Rosenberg JE, Kantoff P, Raynaud F, Martins V, Lee G, Kheoh T, Kim J, Molina A, Small EJ. Phase I clinical trial of the CYP17 inhibitor abiraterone acetate demonstrating clinical activity in patients with castration-resistant prostate cancer who received prior ketoconazole therapy. J Clin Oncol, 2010; 28(9):1481. CrossRef

 Samant TS, Dhuria S, Lu Y, Laisney M, Yang S, Grandeury A, Mueller-Zsigmondy M, Umehara K, Huth F, Miller M, Germa C, Elmeliegy M. Ribociclib bioavailability is not affected by gastric ph changes or food intake: in silico and clinical evaluations. Clin Pharmacol Ther, 2018; 104(2):374–83. CrossRef

 Shono Y, Jantratid E, Kesisoglou F, Reppas C, Dressaman, JB. Forecasting in vivo oral absorption and food effect of micronized and nanosized aprepitant formulations in humans. Eur J Pharm Biopharm, 2010; 76(1):95–104. CrossRef

 Shono Y, Jantraid E, Janssen N, Kesisoglou F, Mao Y, Vertzoni M, Reppas C, Dressman JB. Prediction of food effects on the absorption of celecoxib based on biorelevant dissolution testing coupled with physiologically based pharmacokinetic modeling. Eur J Pharm Biopharm, 2009; 73(1):107–14. CrossRef

 Shono Y, Jantratid E, Dressman JB. Precipitation in the small intestine may play a more important role in the in vivo performance of poorly soluble weak bases in the fasted state: case example nelfinavir. Eur J Pharm Biopharm, 2011; 79(2):349–56. CrossRef

 Sinisi S, Alimguzhin V, Mancini T, Tronci E, Leeners B. Complete populations of virtual patients for in silico clinical trials. Bioinformatics, 2020; 36(22–3):5465–5472. CrossRef

 Tanaka C, Yin OQ, Sethuraman V. Clinical pharmacokinetics of the BCR-ABL tyrosine kinase inhibitor nilotinib. Clin Pharmacol Ther, 2010; 87(2):197–203. CrossRef

 Van Hest R, Mathot R, Vulto A, Weimar W, Van Gelder T. Predicting the usefulness of therapeutic drug monitoring of mycophenolic acid: a computer simulation. Ther Drug Monit, 2005; 27(2):163–7. CrossRef

 Wang B, Higgs BW, Chang L, Vainshtein I, Liu Z, Streicher K, Liang M, White WI, Yoo S, Richman L, Jallal B, Roskos L, Yao Y. Pharmacogenomics and translational simulations to bridge indications for an anti-interferon-α receptor antibody. Clin Pharmacol Ther, 2013; 93(6):483–92. CrossRef

 Xia B, Heimbach T, Lin TH, Li S, Zhang H, Sheng J, He H. Utility of physiologically based modeling and preclinical in vitro/in vivo data to mitigate positive food effect in a BCS class 2 compound. AAPS PharmSciTech, 2013; 14(3):1255–66.

 Xiao Q, Tang L, Xu R, Qian W, Yang J. Physiologically based pharmacokinetics model predicts the lack of inhibition by repaglinide on the metabolism of pioglitazone. Biopharm Drug Dispos, 2015; 36(9):603–12. CrossRef

 Yu Y, Loi CM, Hoffman J, Wang D. Physiologically based pharmacokinetic modeling of palbociclib. J Clin Pharmacol, 2017; 57(2):173–84. CrossRef

 Zhang C, Wang WW, Pan MM, Gu ZC. Simulation of anticoagulation in atrial fibrillation patients with rivaroxaban—from trial to target population. Rev Cardiovasc Med, 2011; 22(3):1019–27. CrossRef

 Zytiga. [Bula]. Toronto: Patheon Inc. 2013. [Online]. Available via http://www.accessdata.fda.gov/drugsatfda_docs/label/2011/202379lbl.pdf (Accessed: 02 March 2022).

Reference

Albers S, Meibohm B, Mir TS, Laer S. Population pharmacokinetics and dose simulation of carvedilol in pediatric patients with congestive heart failure. Br J Clin Pharmacol, 2008; 65(4):511-22. https://doi.org/10.1111/j.1365-2125.2007.03046.x

Amin A, Keshishian A, Trocio J, Dina O, Le H, Rosenblatt L, Liu X, Mardekian J, Zhang Q, Baser O, Vo L. Risk of stroke/systemic embolism, major bleeding and associated costs in non-valvular atrial fibrillation patients who initiated apixaban, dabigatran or rivaroxaban compared with warfarin in the United States Medicare population. Curr Med Res Opin, 2017; 33(9):1595-604. ANMAT. Nuevos aranceles para mantenimiento de registros de productos médicos y especialidades medicinales [Online]. Available via https://www.argentina.gob.ar/noticias/ nuevos-aranceles-para-mantenimiento-de-registros-de-productos-medicos-y-especialidades (Accessed 13 February 2022). https://doi.org/10.1080/03007995.2017.1345729

ANVISA. Protocolo de segurança e eficácia de medicamentos inovadores. 2021 [Online]. Available via https://www.gov.br/anvisa/pt-br/setorregulado/regularizacao/ medicamentos/informes/medicamentos-sinteticos/protocolo-de-seguranca-e-eficacia-de-medicamentos-inovadores (Accessed 13 February 2022).

Berndt ER, Nass D, Kleinrock M, Aitken M. Decline in economic returns from new drugs raises questions about sustaining innovations. Health Affairs, 2015; 34(2):245-52. https://doi.org/10.1377/hlthaff.2014.1029

Camm AJ, Amarenco P, Haas S, Hess S, Kirchhof, Kuhls S, van Eickels M, Turpie AG, XANTUS Investigators. XANTUS: a real-world, prospective, observational study of patients treated with rivaroxaban for stroke prevention in atrial fibrillation. Eur Heart J, 2016; 37(14):1145-53. https://doi.org/10.1093/eurheartj/ehv466

Cardozo L, Khullar V, El-Tahtawy A, Guan Z, Malhotra B, Staskin D. Modeling dose-response relationships of the effects of fesoterodine in patients with overactive bladder. BMC Urol, 2010; 10(1):1-11. https://doi.org/10.1186/1471-2490-10-14

Carvalho ARD. Método Monte Carlos e suas aplicações. 122 f. Dissertação (Mestrado em Matemática), Universidade Federal de Roraima, Boa Vista, Brazil, 2017.

Certara. SIMCYP PBPK Simulator. Versão 21. Phoenix: Certara, 2022 [Online]. Available via https://www.certara.com/ software/simcyp-pbpk/ (Accessed 4 April 2022).

Clermont G, Bartels SJ, Kumar R, Constantine G, Vodovotz Y, Chow C. In silico design of clinical trials: a method coming of age. Crit Care Med, 2004; 32(10):2061-70. https://doi.org/10.1097/01.CCM.0000142394.28791.C3

Cuadros DF, Abu-Raddad LJ, Awad SF, García-Ramos G. Use of agent-based simulations to design and interpret HIV clinical trials. Comput Biol Med, 2014; 50:1-8. https://doi.org/10.1016/j.compbiomed.2014.03.008

Dimasi JA, Hansen RW, Grabwski HC, Lasagna L. Research and development costs for new drugs by therapeutic category. Pharm Econ, 1995; 7(2):152-69. https://doi.org/10.2165/00019053-199507020-00007

EMA. Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation, 2016 [Online]. Available via http://www.ema.europa. eu/docs/en_GB/document_library/Scientific_guideline/2016/07/ WC500211315.pdf (Accessed 27 May 2022).

EMA. Research and development [Online]. Available via https://www.ema.europa.eu/en/human-regulatory/research-development (Accessed 13 February 2022).

FDA. Guidance for industry: physiologically based pharmacokinetic analyses-format and content, 2016 [Online]. Available via https://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ UCM531207.pdf (Accessed 27 May 2022).

FDA. Learn about clinical studies. 2019. [Online]. Available via https://clinicaltrials.gov/ct2/about-studies/learn (Accessed 13 February 2022).

FDA. The drug development process. Step 3: Clinical research. FDA, 2018. [Online]. Available via https://www.fda. gov/patients/drug-development-process/step-3-clinical-research (Accessed 13 February 2022).

Federico MP, Sakata RAP, Pinto PFC, Furtado GHC. Noções sobre parâmetros farmacocinéticos/farmacodinâmicos e sua utilização na prática médica. Rev Soc Brasil Clín Méd, 2017; 15(3):201-5.

Fransson M, Gréen H. Comparison of two types of population pharmacokinetic model structures of paclitaxel. Eur J Pharm Sci, 2008; 33(2):128-37. https://doi.org/10.1016/j.ejps.2007.10.005

Friberg LE, De Greef R, Kerbusch T, Karlsson MO. Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Clin Pharm Ther, 2009; 86(1):84-91. https://doi.org/10.1038/clpt.2009.44

Gidal BE, Maganti R, Laurenza A, Yang H, Verbel DA, Schuck E, Ferry J. Effect of enzyme inhibition on perampanel pharmacokinetics: why study design matters. Epilepsy Res, 2017; 134:41-8. https://doi.org/10.1016/j.eplepsyres.2017.04.018

Graham DJ, Reichman ME, Wernecke M, Zhang R, Southworth MR, Levenson M, ... Kelman JA. Cardiovascular, bleeding, and mortality risks in elderly medicare patients treated with dabigatran or warfarin for nonvalvular atrial fibrillation. Circulation, 2015; 131(2):157-64. https://doi.org/10.1161/CIRCULATIONAHA.114.012061

ICH. M4 Organisation of the common technical document for the registration of pharmaceuticals for human use guidance for industry. ICH, Geneva, Switzerland, 2004.

Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp® population-based ADME simulator. Expert Opin Drug Metab Toxicol, 2009; 5(2):211-23. https://doi.org/10.1517/17425250802691074

Jensen EJ. Research expenditures and the discovery of new drugs. J Indus Econ, 1987; 36:83-95. https://doi.org/10.2307/2098598

Jereb R, Opara J, Bajc A, Petek B. Evaluating the impact of physiological properties of the gastrointestinal tract on drug in vivo performance using physiologically based biopharmaceutics modeling and virtual clinical trials. J Pharm Sci, 2021; 110(8):3069-81. https://doi.org/10.1016/j.xphs.2021.04.007

Ji Z, Yan K, Li W, Hu H, Zhu X. Mathematical and computational modeling in complex biological systems. BioMed Res Int, 2017; 2017:1-16. https://doi.org/10.1155/2017/5958321

Kar S, Leszczynski J. Recent advances of computational modeling for predicting drug metabolism: a perspective. Curr Drug Metab, 2017; 18(12):1106-22. https://doi.org/10.2174/1389200218666170607102104

Kato T, Nakagawa H, Mikkaichi T, Miyano T, Matsumoto Y, Ando S. Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches. Eur J Pharm Sci, 2020; 151:45-52. https://doi.org/10.1016/j.ejpb.2020.03.012

Kowalski KG, Olson S, Remmers AE, Hutmacher MM. Modeling and simulation to support dose selection and clinical development of SC-75416, a selective COX-2 inhibitor for the treatment of acute and chronic pain. Clin Pharmacol Ther, 2008; 83(6):857-66. https://doi.org/10.1038/sj.clpt.6100374

Laliberté F, Cloutier M, Nelson WW, Coleman CI, Pilon D, Olson. Real-world comparative effectiveness and safety of rivaroxaban and warfarin in nonvalvular atrial fibrillation patients. Curr Med Res Opin, 2014; 30(7):1317-25. https://doi.org/10.1185/03007995.2014.907140

Li CH, Sherer EA, Lewis LD, Bies RR. Clinical trial simulation to evaluate population pharmacokinetics and food effect: capturing abiraterone and nilotinib exposures. J Clin Pharmacol, 2015; 55(5):556-62. https://doi.org/10.1002/jcph.449

Litou C, Patel N, Turner DB, Kostewicz E, Kuentz M, Box KJ, Dressman J. Combining biorelevant in vitro and in silico tools to simulate and better understand the in vivo performance of a nano-sized formulation of aprepitant in the fasted and fed states. Eur J Pharm Sci, 2019; 138:105031. Ma L, Zhao L, Xu Y, Yim S, Doddapaneni S, Sahajwalla CG, Wang Y, Ji P. Clinical end point sensitivity in rheumatoid arthritis: modeling and simulation. J Pharmacokinet Pharmacodyn, 2014; 41(5):537-43. https://doi.org/10.1016/j.ejps.2019.105031

Mancini T, Mari F, Massini A, Melatti I, Salvo I, Sinisi S, Tronci E, Ehrig R, Röblitz S, Leeners B. Computing personalised treatments through in silico clinical trials. A case study on down regulation in assisted reproduction. Intell Artif, 2018; 2271:1-16. https://doi.org/10.29007/g864

Mollmann H, Wagner M, Krishnaswami S, Dimova H, Tang Y, Falcoz C, Daley-Yates PT, Krieg M, Stöckmann R, Barth J, Lawlor C, Möllmann AC, Derendorf H, Hochhaus G. Single-dose and steady-state pharmacokinetic and pharmacodynamic evaluation of therapeutically clinically equivalent doses of inhaled fluticasone propionate and budesonide, given as Diskus or Turbo haler dry-powder inhalers to healthy subjects. J Clin Pharmacol, 2001; 52(5):487-95. https://doi.org/10.1177/00912700122012913

Murthy BP, Skee DM, Danyluk AP, Brett V, Vorsanger GJ, Moskovitz BL. Pharmacokinetic model and simulations of dose conversion from immediate-to extended-release tramadol. Curr Med Res Opin, 2007; 23(2):275-84. https://doi.org/10.1185/030079906X162773

Nathan RA, Berger W, Yang W, Cheema A, Silvey M, Wu W, Philpot E. Effect of once-daily fluticasone furoate nasal spray on nasal symptoms in adults and adolescents with perennial allergic rhinitis. Ann Allerg Asth Immunol, 2008; 100(5):497-505. https://doi.org/10.1016/S1081-1206(10)60477-2

Naimi B, Voinov A. Stella R: a software to translate Stella models into R open-source environment. Environ Modell Software, 2012; 38:117-8. https://doi.org/10.1016/j.envsoft.2012.05.012

Najafzadeh M, Schneeweiss S, Choudhry NK, Wang SV, Gagne JJ. Simulation for predicting effectiveness and safety of new cardiovascular drugs in routine care populations. Clin Pharmacol Ther, 2018; 104(5):1008-15. https://doi.org/10.1002/cpt.1045

Neil N, Merchant S, Provenzano D, Ogden K, Mody SH. Clinical simulation model of long-acting opioids for treatment of chronic non-cancer pain in the United States. J Med Econ, 2013; 16(2):307-17. https://doi.org/10.3111/13696998.2012.756401

Page MJ, Moher D. Extensions in development: Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Syst Rev, 2018; 6(1):1-14. https://doi.org/10.1186/s13643-017-0663-8

Pappalardo F, Russo G, Tshinanu FM, Viceconti M. In silico clinical trials: concepts and early adoptions. Brief Bioinform, 2019; 20(5):1699-708. https://doi.org/10.1093/bib/bby043

Parrott NJ, Yu LJ, Takano R, Nakamura M, Morcos PN. Physiologically based absorption modeling to explore the impact of food and gastric pH changes on the pharmacokinetics of alectinib. AAPS J, 2016; 18(6):1464-74. https://doi.org/10.1208/s12248-016-9957-3

Patel A, Wilson R, Harrell AW, Taskar KS, Taylor M, Tracey H, Riddell K, Georgiou A, Cahn AP, Marotti M, Hessel EM. Drug interactions for low-dose inhaled nemiralisib: a case study integrating modeling, in vitro, and clinical investigations. Drug Metab Dispos, 2020; 48(4):307-16. https://doi.org/10.1124/dmd.119.089003

Patel MR, Mahaffey KW, Garg J, Pan G, Singer De, Hacke W. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med, 2011; 365(10):883-91. https://doi.org/10.1056/NEJMoa1009638

Patsalos PN. The clinical pharmacology profile of the new antiepileptic drug perampanel: a novel noncompetitive AMPA receptor antagonist. Epilepsia, 2015; 56(1):12-27. https://doi.org/10.1111/epi.12865

Peters MD, Godfrey C, McInerney P, Khalil H, Larsen P, Marnie C, Pollock D, Tricco AC, Munn Z. Best practice guidance and reporting items for the development of scoping review protocols. JBI Evid Synth, 2022; 20(4), 953-968. https://doi.org/10.11124/JBIES-21-00242

Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: Scoping reviews. In: Aromataris E, Munn Z (Eds.). JBI Manual for evidence synthesis. JBI, 2020. [Online] Available via https://synthesismanual.jbi.global (Accessed 20 February 2022). https://doi.org/10.46658/JBIRM-20-01

PMDA. History [Online]. Available via https://www. pmda.go.jp/english/about-pmda/outline/0002.html (Accessed 13 February 2022).

Rebeka J, Jerneja O, Igor L, Boštjan P, Aleksander B, Simon Ž, Albin K. PBPK absorption modeling of food effect and bioequivalence in Fed State for two formulations with crystalline and amorphous forms of BCS 2 class drug in generic drug development. AAPS PharmSciTech, 2019; 20(2):1-10. https://doi.org/10.1208/s12249-018-1285-8

Ryan CJ, Smith MR, Fong L, Rosenberg JE, Kantoff P, Raynaud F, Martins V, Lee G, Kheoh T, Kim J, Molina A, Small EJ. Phase I clinical trial of the CYP17 inhibitor abiraterone acetate demonstrating clinical activity in patients with castration-resistant prostate cancer who received prior ketoconazole therapy. J Clin Oncol, 2010; 28(9):1481. https://doi.org/10.1200/JCO.2009.24.1281

Samant TS, Dhuria S, Lu Y, Laisney M, Yang S, Grandeury A, Mueller-Zsigmondy M, Umehara K, Huth F, Miller M, Germa C, Elmeliegy M. Ribociclib bioavailability is not affected by gastric ph changes or food intake: in silico and clinical evaluations. Clin Pharmacol Ther, 2018; 104(2):374-83. https://doi.org/10.1002/cpt.940

Shono Y, Jantratid E, Kesisoglou F, Reppas C, Dressaman, JB. Forecasting in vivo oral absorption and food effect of micronized and nanosized aprepitant formulations in humans. Eur J Pharm Biopharm, 2010; 76(1):95-104. https://doi.org/10.1016/j.ejpb.2010.05.009

Shono Y, Jantraid E, Janssen N, Kesisoglou F, Mao Y, Vertzoni M, Reppas C, Dressman JB. Prediction of food effects on the absorption of celecoxib based on biorelevant dissolution testing coupled with physiologically based pharmacokinetic modeling. Eur J Pharm Biopharm, 2009; 73(1):107-14. https://doi.org/10.1016/j.ejpb.2009.05.009

Shono Y, Jantratid E, Dressman JB. Precipitation in the small intestine may play a more important role in the in vivo performance of poorly soluble weak bases in the fasted state: case example nelfinavir. Eur J Pharm Biopharm, 2011; 79(2):349-56. https://doi.org/10.1016/j.ejpb.2011.04.005

Sinisi S, Alimguzhin V, Mancini T, Tronci E, Leeners B. Complete populations of virtual patients for in silico clinical trials. Bioinformatics, 2020; 36(22-3):5465-5472. https://doi.org/10.1093/bioinformatics/btaa1026

Tanaka C, Yin OQ, Sethuraman V. Clinical pharmacokinetics of the BCR-ABL tyrosine kinase inhibitor nilotinib. Clin Pharmacol Ther, 2010; 87(2):197-203. https://doi.org/10.1038/clpt.2009.208

Van Hest R, Mathot R, Vulto A, Weimar W, Van Gelder T. Predicting the usefulness of therapeutic drug monitoring of mycophenolic acid: a computer simulation. Ther Drug Monit, 2005; 27(2):163-7. https://doi.org/10.1097/01.ftd.0000158083.45954.97

Wang B, Higgs BW, Chang L, Vainshtein I, Liu Z, Streicher K, Liang M, White WI, Yoo S, Richman L, Jallal B, Roskos L, Yao Y. Pharmacogenomics and translational simulations to bridge indications for an anti-interferon-α receptor antibody. Clin Pharmacol Ther, 2013; 93(6):483-92. https://doi.org/10.1038/clpt.2013.35

Xia B, Heimbach T, Lin TH, Li S, Zhang H, Sheng J, He H. Utility of physiologically based modeling and preclinical in vitro/in vivo data to mitigate positive food effect in a BCS class 2 compound. AAPS PharmSciTech, 2013; 14(3):1255-66. Xiao Q, Tang L, Xu R, Qian W, Yang J. Physiologically based pharmacokinetics model predicts the lack of inhibition by repaglinide on the metabolism of pioglitazone. Biopharm Drug Dispos, 2015; 36(9):603-12. https://doi.org/10.1002/bdd.1987

Yu Y, Loi CM, Hoffman J, Wang D. Physiologically based pharmacokinetic modeling of palbociclib. J Clin Pharmacol, 2017; 57(2):173-84. https://doi.org/10.1002/jcph.792

Zhang C, Wang WW, Pan MM, Gu ZC. Simulation of anticoagulation in atrial fibrillation patients with rivaroxaban- from trial to target population. Rev Cardiovasc Med, 2011; 22(3):1019-27. https://doi.org/10.31083/j.rcm2203111

Zytiga™. [Bula]. Toronto: Patheon Inc. 2013. [Online]. Available via http://www.accessdata.fda.gov/drugsatfda_docs/ label/2011/202379lbl.pdf (Accessed: 02 March 2022).

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