1. Introduction

2. Materials and Methods

3. Results

3.1. Systematic Literature Search and Study Selection Process

A systematic literature search was conducted to identify relevant studies exploring virtual bioequivalence through in silico tools. The identification phase began with a comprehensive search across databases (PubMed, Scopus, Web of Science, and Cochrane Library), resulting in 6,472 records (Figure 1). Additionally, 5 records were found through external sources, including a Google search and manual journal hand searches. After the removal of duplicates, 5,651 records remained for screening. During the screening phase, a title-based screening of the 5,651 records was performed. This stage resulted in the exclusion of 4,595 records that were deemed irrelevant based on their titles, leaving 1,056 records for abstract screening. Upon reviewing the abstracts, 839 records were further excluded for not meeting the inclusion criteria. The remaining 217 records proceeded to full-text assessment in the eligibility phase. At the full-text screening stage, 162 articles were excluded, each for specific reasons: 64 articles did not involve in silico tools, 54 articles lacked any connection to clinical trials, 32 articles were unrelated to health outcomes, and 12 articles failed to provide sufficient data. This rigorous assessment led to the inclusion of 55 studies in the final scoping review.

The results of this review demonstrate that despite the initial large number of records identified, only a small fraction of studies met the stringent inclusion criteria for assessing virtual bioequivalence. The high rate of exclusion highlights the specificity of the focus on in silico methodologies and the limited availability of studies directly comparing computational tools with traditional clinical bioequivalence trials. One significant finding was the exclusion of a substantial number of studies that did not utilize in silico tools (64 records), which underscores the emerging but still nascent nature of computational methods in bioequivalence assessment. Furthermore, the exclusion of studies not involving clinical trials (54 records) points to the challenge of integrating virtual bioequivalence tools into clinical research, as many studies may focus solely on theoretical modeling without validation in real-world clinical settings. The exclusion of studies due to insufficient data (12 records) also emphasizes the importance of robust data reporting and transparency in in silico research. For virtual bioequivalence to gain widespread regulatory acceptance, comprehensive data and validation will be crucial. In addition to the primary studies included in this scoping review, regulatory guidelines and frameworks regarding virtual bioequivalence were retrieved from major regulatory bodies, notably the FDA and EMA. Both organizations have established guidelines for bioequivalence assessments, but specific provisions related to the use of in silico tools in these assessments are still evolving.

Table 1. A summary of the studies included in the scoping review outlines the pharmaceutical drugs, therapeutic indications, in silico tools used, and key findings related to bioequivalence.

Table 1. A summary of the studies included in the scoping review outlines the pharmaceutical drugs, therapeutic indications, in silico tools used, and key findings related to bioequivalence.

Study; Year	Database	Pharmaceutical Drug(s)	Drug(s) Indication Class	In Silico Tool(s)	Bioequivalence Studies (Main Findings)
Yu, Y. et al. [26], 2017	Pubmed	Palbociclib	Anticancer	SimCYP® version 14	The study developed an in silico PBPK model of palbociclib, predicting that moderate CYP3A inhibitors can increase its AUC by ~40% and inducers can decrease it by ~40%, with most predicted vs observed discrepancies within 20%.
Cho, CK. et al. [27], 2021	Pubmed	Tamsulosin	Benign prostatic hyperplasia (BPH)	SimCYP®	This study developed and validated an in silico PBPK model of tamsulosin for different CYP2D6 genotypes. The model predicts that tamsulosin exposure in CYP2D6*wt/10 and CYP2D610/10 genotypes is 1.23- and 1.76-fold higher, respectively than in CYP2D6wt/*wt, contributing to personalized pharmacotherapy by predicting pharmacokinetics based on CYP2D6 genotype.
Chen, G. et al. [28], 2023	Pubmed	Maribavir	Anti-cytomegalovirus	SimCYP® version 17	This study developed and validated an in silico PBPK model of maribavir, predicting that strong or moderate CYP3A4 inducers significantly reduce maribavir exposure (with rifampin decreasing AUC by 60%), while CYP3A4 inhibitors have no clinically significant effect, guiding dosing adjustments for personalized therapy in patients with CMV infection.
Kim YH, et al. [29], 2021	Pubmed	Celecoxib	NSAIDs	PK-Sim® version 7.2	This study developed an in silico PBPK model of celecoxib based on CYP2C9 genetic polymorphism. It successfully predicted pharmacokinetics across different genotypes, demonstrating its potential for personalized dosing and reducing adverse drug reactions in precision medicine.
Fendt R, et al. [30], 2021	Pubmed	Caffeine	Neurostimulant	PK-Sim® version 8.0	This study demonstrates that personalized PBPK models for caffeine, incorporating individual demographics, physiology, and CYP1A2 phenotype, significantly improve pharmacokinetic predictions, increasing accuracy from 45.8% to 66.15% within the 1.25-fold range of observed values, highlighting their potential for model-informed precision dosing in clinical practice.
Watanabe A, et al. [31], 2021	Pubmed	Esaxerenone	Antimineralocorticoid	Simcyp®, version 17	This study developed a PBPK model for esaxerenone that accurately predicts drug-drug interactions (DDIs) with CYP3A modulators in healthy subjects and those with hepatic impairment, demonstrating that the model’s predictions for plasma exposure changes—such as a 1.53-fold increase with itraconazole and a 0.31-fold decrease with rifampicin—align closely with observed data, highlighting the need for caution when coadministering CYP3A modulators, especially in patients with hepatic impairment.
Ou Y, et al. [32], 2018	Pubmed	Oprozomib	Antitumor	SimCYP® version 13.2	In this study on oprozomib, the clinical DDI study demonstrated no treatment-related adverse events leading to discontinuation. The PBPK model predicted that a 300 mg dose of oprozomib would not cause a clinically significant change in the exposure of CYP3A4 substrates (≤30%), a prediction that was confirmed by the clinical results. These findings suggest that oprozomib has a low potential to inhibit the metabolism of CYP3A4 substrates in humans, supporting its safe use in combination therapies.
Yee KL, et al. [33], 2020	Pubmed	Doravirine	Antiretroviral	SimCYP® version 17	The study on doravirine showed that coadministration with rifabutin, a moderate CYP3A4 inducer, significantly decreased doravirine exposure. A dose adjustment from 100 mg once daily to 100 mg twice daily was recommended. A PBPK modeling indicated that while CYP3A induction increased doravirine clearance by up to 4.4-fold, M9 exposure increased only by 1.2-fold. A 2.4-fold increase in M9 exposure was anticipated with the adjusted dosing. Subsequent clinical trials confirmed that doravirine and M9 exposures matched model predictions, supporting the new dosing recommendation when administered with rifabutin.
Jo H, et al. [34], 2021	Pubmed	Dapagliflozin	Antidiabetic	SimCYP® version 18	The results demonstrated that the PBPK model for dapagliflozin met the twofold acceptance criteria for model-predicted versus observed drug exposures and pharmacokinetic parameters (AUC and maximum drug concentration) across various scenarios, including monotherapy in healthy adults, patients with hepatic or renal impairment, and drug-drug interactions with UGT1A9 modulators like mefenamic acid and rifampin.
Posada M, et al. [35], 2017	Pubmed	Baricitinib	Antirheumatic	SimCYP® version 13.2	This study demonstrated that baricitinib, an oral selective Janus kinase 1 and 2 inhibitor, has its renal clearance effectively modeled using PBPK modeling, revealing that probenecid, a strong OAT3 inhibitor, increased baricitinib’s AUC(0–∞) by twofold and decreased renal clearance to 69% of control, while predictions indicated that clinically relevant drug-drug interactions with ibuprofen and diclofenac are unlikely, as their in vitro IC50 values suggested AUC(0–∞) ratios of 1.2 and 1.0 for baricitinib.
Zane NR, et al. [36], 2014	Pubmed	Voriconazole	Antifungal	SimCYP® Paediatric	This study developed a PBPK model for voriconazole, accurately predicting pharmacokinetic parameters in adults within a 20% prediction error; however, the pediatric oral model initially overestimated oral bioavailability twofold, which improved after incorporating intestinal first-pass metabolism. This indicates that voriconazole undergoes differential first-pass metabolism in children compared to adults.
Lang J, et al. [37], 2020	Pubmed	Ivabradine	Antianginal and antiischemic	SimCYP® version 18	This study developed a joint parent-metabolite PBPK and pharmacodynamic model for ivabradine and its metabolite, successfully predicting pharmacokinetics and heart rate reductions after intravenous or oral administration, including drug-drug interactions with CYP3A4 inhibitors. The model predicted 92% and 85% of the AUC ratios for ivabradine and its metabolite within acceptable limits, with observed versus predicted heart rate reductions of -7.7/-5.9 bpm and -15.8/-14.0 bpm in control and ketoconazole groups, respectively, establishing a scalable framework for assessing DDI risks in different populations.
Hanke N, et al. [38], 2017	Pubmed	Zoptarelin doxorubicin	Anticancer	PK-Sim® and MoBi®	This study established a PBPK model for zoptarelin doxorubicin, a fusion of doxorubicin and a luteinizing hormone-releasing hormone receptor agonist, to assess its DDI potential. The model, built in two steps—first for doxorubicin and then for zoptarelin doxorubicin—predicted minimal in vivo inhibition of drug transporters OATP1B3 (0.5%) and OCT2 (2.5%). Simulations indicated that co-administration with simvastatin and metformin would not significantly alter their plasma concentrations, demonstrating that zoptarelin doxorubicin has no potential for DDIs via these transporters.
Chen Y, et al. [39], 2017	Pubmed	Gefitinib	Anticancer	SimCYP® version 14	This study developed and validated a PBPK model to compare gefitinib exposure in CYP2D6 ultrarapid metabolizers (UM) and extensive metabolizers (EM). The model predicted a 39% decrease in gefitinib AUC in UM compared to EM, though this reduction remained above the IC90 for EGFR mutations in NSCLC. The model, calibrated with itraconazole-gefitinib interaction data, was validated with clinical data, showing that CYP2D6 system components were accurately modeled. Overall, the reduced exposure in UMs is unlikely to impact gefitinib’s clinical efficacy.
Gajewska M, et al. [40], 2020	Pubmed	Alpelisib	Anticancer	GastroPlus™ version 9.6	This study developed a PBPK model for alpelisib to simulate oral absorption and plasma pharmacokinetics in healthy subjects, successfully predicting bioequivalence outcomes between clinical and commercial formulations under various conditions (fasted, fed, and altered pH). The model incorporated in vitro dissolution data, and its predictive errors for plasma Cmax and AUC were ≤30%, making it a valuable tool for virtual bioequivalence assessments.
Donovan MD, et al. [41], 2018	Pubmed	Bumetanide	Cardiovascular disease	SimCYP® version 14	This study developed a PBPK model to predict bumetanide’s plasma and brain concentrations in adult and pediatric populations. While the model accurately predicted pharmacokinetic parameters for adults and older children within two-fold of observed values, it failed to fit well with neonatal data. The study highlights the need for more metabolic and transport parameter data before the model can be reliably used to predict bumetanide disposition and recommend dosing in neonates.
Fu Q, et al. [42], 2021	Pubmed	Lenabasum	Anti-inflammatory	SimCYP® version 19	A PBPK model for lenabasum, a synthetic CB2 agonist, was developed using clinical data and CYP metabolism parameters. The model accurately predicted lenabasum’s AUC and Cmax within 1.19- and 1.25-fold of observed values. Simulations indicated that rifampin (a CYP inducer) would decrease lenabasum’s AUC by 64%, while fluconazole (a CYP inhibitor) would increase AUC by 43%. The model effectively predicts lenabasum pharmacokinetics and can guide dose adjustments in drug-drug interaction scenarios.
Bergagnini-Kolev M, et al. [43], 2023	Pubmed	Itraconazole	Antifungal	SimCYP®	A PBPK model was developed to assess the DDI potential of inhaled PUR1900, a dry powder itraconazole formulation, using midazolam as a “victim drug.” Simulations predicted minimal DDI risk, with midazolam’s Cmax and AUC increasing by only 14% and 26%, respectively, when co-administered with 40 mg PUR1900. The low systemic itraconazole exposure from PUR1900 suggests minimal CYP3A4 inhibition, indicating that PUR1900 poses a low DDI risk and may be safely used for pulmonary fungal infections alongside other medications contraindicated with oral itraconazole.
Nakamaru Y, et al. [44], 2015	Pubmed	Teneligliptin	Antidiabetic	SimCYP®	A PBPK model for teneligliptin was developed and validated using the Simcyp simulator. The model accurately predicted plasma concentrations from clinical trials across various populations, including those with renal or liver impairment. The model effectively simulated drug-drug interactions, such as a 2.1—to 2.2-fold increase in teneligliptin exposure when co-administered with the CYP3A4 inhibitor ketoconazole. This robust PBPK model provides detailed insights into the pharmacokinetics of teneligliptin, allowing the prediction of drug-drug interactions and exposure changes in specific patient populations.
Thompson EJ, et al. [45], 2024	Pubmed	Pantoprazole	GERD	PK-Sim® version 10.0	This study developed a PBPK model for pantoprazole, extending it to children with obesity while accounting for genetic variation in CYP2C19 and physiological changes related to age and obesity. The model evaluated three dosing strategies and found that FDA-recommended weight-tiered dosing resulted in the most consistent pantoprazole exposure across children, regardless of obesity or CYP2C19 phenotype. The findings demonstrate the utility of PBPK models in optimizing dosing for pediatric populations where clinical trial data may be limited, particularly in children with obesity.
Wang HY, et al. [46], 2016	Pubmed	Midazolam	Hypnotic-sedative	SimCYP® version 13	This study developed and evaluated a PBPK model using the SimCYP simulator to predict the pharmacokinetics of midazolam in Chinese subjects across different age groups. The model accurately predicted midazolam plasma concentrations, AUC, and Cmax following oral administration, with predicted-to-observed clearance ratios ranging from 0.86 to 1.12. The findings demonstrate that the SimCYP PBPK model can effectively predict CYP3A4/5-mediated pharmacokinetics in the Chinese population, providing valuable insights for designing bridging clinical trials and optimizing drug dosing for different ethnic groups.
Callegari E, et al. [47], 2021	Pubmed	Ertugliflozin	Antidiabetic	SimCYP® version 15	This study utilized PBPK modeling to predict a 1.51-fold increase in the area under the plasma concentration-time curve (AUCR) for ertugliflozin when co-administered with the UGT inhibitor mefenamic acid (MFA). This demonstrated the model’s effectiveness in evaluating drug-drug interactions and virtual bioequivalence for UGT substrates.
Nakamura T, et al. [48], 2018	Pubmed	Tamoxifen	Anticancer	MATLAB version 8.0.0.783 (R2012b)	This study used PBPK modeling and virtual clinical study (VCS) simulations to predict the outcomes of the Tamoxifen Response by CYP2D6 Genotype-based Treatment-1 (TARGET-1) trial, which investigated tamoxifen dosing guided by CYP2D6 genotypes. The simulations indicated an average probability of 0.469 for demonstrating the superior efficacy of escalated tamoxifen doses in CYP2D6 variant carriers, which increased to 0.674 with a larger sample size (n = 260). The analyses highlighted that variability in endoxifen levels negatively impacted the likelihood of achieving the study’s endpoint, emphasizing the value of PBPK modeling and VCS in optimizing clinical trial design.
Stader F, et al. [49], 2021	Pubmed	Bictegravir	Antiretroviral	Matlab 2017a	This study developed a PBPK model for bictegravir to assess the impact of aging on its pharmacokinetics in people living with HIV (PLWH). The model validated with data from young (20-55 years) and elderly (55-85 years) PLWH indicated that bictegravir exposure remained unchanged with age. Simulations suggested a potential 40% increase in drug exposure across adulthood, consistent with age-related changes seen in other drugs. Thus, no dose adjustment for bictegravir is necessary in elderly PLWH without severe comorbidities.
Yang R, et al. [50], 2024	Pubmed	Omeprazole	GERD	SimCYP®	This study developed PBPK and in vitro-in vivo relationship (IVIVR) models for enteric-coated omeprazole capsules, aiming to explore VBE for biological exemptions. The predicted pharmacokinetics matched observed data, establishing clinically relevant dissolution specifications (CRDS) for bioequivalence. It required 48 healthy subjects and ensured dissolution rates of 28%-54%, 52%, and 80% after two, three, and six hours, respectively. This approach can facilitate biological exemptions for other BCS class II generics and improve drug development efficiency.
Ojala K, et al. [51], 2020	Pubmed	ODM-204	Anticancer	GastroPlus version 9.7	This study demonstrated that in vitro dissolution tests, the TIM-1 intestinal model, and PBPK simulations effectively predicted the absorption properties of the poorly soluble lipophilic weak base ODM-204, providing valuable insights for evaluating its bioavailability and informing formulation decisions.
Agyemang A, et al. [52], 2021	Pubmed	Acumapimod	Anti-inflammatory	SimCYP® version 16.1	The study found that co-administration of the CYP3A4 inhibitors azithromycin and itraconazole with acumapimod did not significantly affect its pharmacokinetics or safety profile, as confirmed by both clinical DDI studies and PBPK modeling. This supports the concomitant use of these inhibitors in patients.
Chen Y, et al. [53], 2014	Pubmed	Amiodarone	Anti-arrhythmic	SimCYP® version 12	The study demonstrates that a PBPK model effectively predicts DDIs involving amiodarone (AMIO) and its major metabolite, mono-desethyl-amiodarone (MDEA), by accurately simulating their pharmacokinetic profiles and accumulation. The model successfully captured the clinically significant increases in exposure of simvastatin (1.2- to 2-fold), dextromethorphan, and warfarin, highlighting the importance of considering inhibitory metabolites in DDI assessments.
Litou C, et al. [54], 2019	Pubmed	Aprepitant	Antiemetic	SimCYP® version 16.1	This study highlights the development of EMEND®, a nano-sized aprepitant formulation, using innovative biopharmaceutical tools. In vitro tests showed that native surfactants significantly enhanced the aprepitant’s solubility. A PBPK model in the Simcyp Simulator accurately simulated plasma concentrations of aprepitant after administering 80 mg and 125 mg capsules in both fasted and fed states. Parameter sensitivity analysis indicated that while nano-sizing improved in vivo performance, intestinal solubility remains a limiting factor for bioavailability, classifying aprepitant as DCS IIb. The findings underscore the value of combining in vitro and in silico methods for predicting absorption and supporting regulatory assessments of poorly soluble compounds.
Einolf HJ, et al. [55], 2017	Pubmed	Sonidegib	Anticancer	SimCYP® version 13.2	This study evaluated the effects of strong CYP3A inhibitors, ketoconazole (KTZ) and rifampin (RIF), on sonidegib (Odomzo) pharmacokinetics (PK) after a single 800 mg dose in healthy subjects. KTZ increased sonidegib exposure by 2.25-fold in terms of area under the curve (AUC), while RIF decreased it by 72%. A validated PBPK model accurately predicted these interactions and indicated that sonidegib would have a more significant drug-drug interaction (DDI) magnitude with CYP3A inhibitors at steady state, informing dosing recommendations in the product label.
Yamazaki S, et al. [56], 2015	Pubmed	Crizotinib	Anticancer	SimCYP® version 13.1	This study developed and refined a PBPK model for crizotinib (Xalkori), accurately predicting its exposure from clinical data. The model verified DDI results from single-dose studies with ketoconazole and rifampin, showing comparable fold-increases in crizotinib exposure during multiple-dose DDI studies. These findings suggest that dose adjustments in multiple-dose scenarios can rely on single-dose outcomes, with the PBPK model applicable for predicting crizotinib exposure in various clinical contexts.
Riddell K, et al. [57], 2020	Pubmed	Molibresib	Anticancer	SimCYP® version 14	This study evaluated the pharmacokinetics of molibresib (GSK525762) in a randomized DDI trial, utilizing PBPK modeling to determine safe dosing in healthy volunteers. Administering 5 mg of molibresib with the strong CYP3A inhibitor, itraconazole increased its AUC by 4.15-fold and Cmax by 66%, while the active metabolites’ AUC and Cmax decreased by 70% and 87%. Subsequently, the dose was increased to 20 mg with the strong CYP3A inducer rifampicin, resulting in a 91% reduction in molibresib’s AUC and an 80% reduction in Cmax, with the metabolites’ AUC decreasing by only 8% and Cmax increasing 2-fold. These findings confirmed that molibresib is a CYP3A4 substrate and demonstrated the efficiency of PBPK modeling in assessing drug-drug interactions.
Purohit V, et al. [58], 2024	Pubmed	Tofacitinib	Antirheumatic	SimCYP® version 20	This study demonstrated bioequivalence between a once-daily modified-release (MR) microsphere formulation of tofacitinib for pediatric patients and a 5 mg twice-daily immediate-release (IR) solution using PBPK virtual BE trials instead of a clinical trial. The verified PBPK model, utilizing the Simcyp ADAM module, incorporated the clinically observed intrasubject coefficient of variation (ICV) to assess BE. Results confirmed BE between the formulations after single and multiple doses, highlighting a strategy that minimizes unnecessary drug exposure for healthy volunteers while facilitating new formulation development.
Dennison TJ, et al. [59], 2017	Pubmed	Aamlodipine and atorvastatin	Cardiovascular disease	SimCYP® version 14	This study developed and characterized orally disintegrating tablets (ODTs) containing amlodipine (5 mg) and atorvastatin (10 mg), evaluating the bioequivalence of single versus fixed-dose combination (FDC) formulations. The ODTs rapidly disintegrated in under 30 seconds, exhibiting strong mechanical properties. In vitro dissolution tests were performed in fasted and fed-state simulated intestinal fluid, showing no significant differences in active pharmaceutical ingredient dissolution, except for amlodipine in fed-state conditions. Pharmacokinetic simulations using the Simcyp model indicated no difference in bioavailability between single and FDC ODTs. However, atorvastatin exhibited increased Cmax and AUC in fed subjects, likely due to altered gut transit and reduced CYP3A4 metabolism.
Knöchel J, et al. [60], 2024	Pubmed	Midazolam	Hypnotic-sedative	SimCYP® version 20	In this study, a validated PBPK model was developed to evaluate the impact of Atuliflapon on the pharmacokinetics of midazolam, a sensitive CYP3A4 substrate, revealing that Atuliflapon is a weak CYP3A4 inhibitor, with the model predicting increases of 27% in AUC and 23% in Cmax for midazolam upon co-administration, thus indicating that Atuliflapon’s minor inhibitory effect is unlikely to affect the pharmacokinetics of CYP3A4-metabolized drugs significantly.
Freise KJ, et al. [61], 2017	Pubmed	Venetoclax	Anticancer	SimCYP® version 14	This study developed and verified a venetoclax PBPK model to predict the impact of cytochrome P450 3A (CYP3A) inhibitors and inducers on venetoclax pharmacokinetics, demonstrating good agreement between predicted and observed pharmacokinetic parameters. The model simulations indicated that moderate and strong CYP3A inducers could significantly decrease venetoclax exposure, while moderate and strong CYP3A inhibitors could increase venetoclax AUC∞ by 100% to 390% and 480% to 680%, respectively. Consequently, recommended dose reductions of at least 50% and 75% for venetoclax are advised when coadministered with moderate and strong CYP3A inhibitors, respectively, to maintain therapeutic exposure levels.
Boetsch C, et al. [62], 2016	Web of Science	Bitopertin	Neurodegenerative disease	GastroPlus™	This study assessed the impact of strong and moderate cytochrome P450 (CYP) 3A4 inhibitors, ketoconazole and erythromycin, on the pharmacokinetics of bitopertin, a glycine reuptake inhibitor primarily metabolized by CYP3A4, through two open-label volunteer studies. Co-administration of ketoconazole increased the bitopertin AUC from 0 to 312 hours by 4.2-fold, while erythromycin increased the AUC from time zero to infinity by 2.1-fold. PBPK modeling predicted AUC ratios that closely matched the observed data, indicating that strong CYP3A4 inhibitors could increase bitopertin AUC0-inf by 7- to 8-fold, while moderate inhibitors could double its AUC0-inf. Consequently, strong CYP3A4 inhibitors should not be administered with bitopertin.
Katsube T, et al. [63], 2020	Web of Science	Lusutrombopag	Thrombocytopenia	SimCYP® version 14	This study evaluated the DDI potential of lusutrombopag, a thrombopoietin receptor agonist, on cytochrome P450 (CYP) 3A activity using midazolam as a probe substrate, and assessed the effect of cyclosporine on lusutrombopag pharmacokinetics through clinical studies and PBPK modeling. Clinical trials showed that lusutrombopag did not significantly affect midazolam’s pharmacokinetics, with mean ratios for maximum plasma concentration (Cmax) and AUC being 1.01 and 1.04, respectively. In contrast, cyclosporine slightly increased lusutrombopag’s Cmax and AUC by 18% and 19%, respectively. Overall, both in vitro and in vivo findings indicated that lusutrombopag has no clinically significant DDI potential with other drugs via CYP3A or P-glycoprotein pathways.
Andreas CJ, et al. [64], 2017	Web of Science	Zolpidem	Hypnotic-sedative	Simcyp® and GastroPlus™	This study explored the absorption of zolpidem, a BCS class I compound, revealing a negative food effect on its pharmacokinetics when administered as immediate or modified release formulations. Using in vitro and in silico methods, including PBPK modeling with Simcyp® and GastroPlus™, the simulations achieved average fold error (AFE) values of less than 1.5. The results indicated that absorption in the fasted state is formulation-controlled, while gastric emptying influences absorption in the fed state, with meal interactions possibly causing incomplete drug release, thereby reducing Cmax and AUC.
Post TM, et al. [65], 2016	Web of Science	Nomegestrol acetate	Hormone therapy	PK-Sim®	This study compared the pharmacokinetics of nomegestrol acetate (NOMAC) in adolescent and adult women after a single dose of NOMAC/E2. No statistically significant differences in NOMAC PK parameters—Cmax, AUC, and half-life (t1/2)—were observed between the two age groups. Additionally, the WB-PBPK model accurately predicted NOMAC AUC and Cmax values for both groups. The findings suggest that NOMAC pharmacokinetics are comparable in adolescents and adults following a single dose, highlighting the model’s utility in addressing ethical challenges in adolescent PK studies.
Li J, et al. [66], 2020	Web of Science	Eliglustat	Gaucher’s disease	SimCYP® version 13	This study evaluated the pharmacokinetics of eliglustat in adults with Gaucher disease type 1 (GD1) and varying CYP2D6 metabolizer phenotypes, particularly those with hepatic and renal impairment. In two Phase 1 studies, a single 84-mg dose of eliglustat was administered. Compared to healthy extensive metabolizers (EM), Cmax and AUC were not significantly different in EMs with mild hepatic impairment, higher in EMs with moderate hepatic impairment, and similar in EMs with severe renal impairment. Based on these results, the eliglustat drug label was revised for patients with hepatic or renal impairment.
Samant TS, et al. [67], 2018	Web of Science	Ribociclib	Anticancer	SimCYP® version 13	This study evaluated the effect of proton pump inhibitors (PPIs) on the bioavailability of ribociclib (KISQALI), a cyclin-dependent kinase 4/6 inhibitor for HR+/HER2- advanced breast cancer, which can be taken with gastric pH-elevating agents and food. Through solubility tests, PBPK modeling, and clinical trial data analysis, results showed no impact of gastric pH on ribociclib pharmacokinetics. This supports labeling that allows coadministration with PPIs. Additionally, bioequivalence with or without a high-fat meal was confirmed, enabling flexible dosing to enhance patient compliance and outcomes.
Morcos PN, et al. [68], 2023	Web of Science	Copanlisib	Anticancer	PK-Sim®	This study evaluated copanlisib, a PI3K inhibitor, in pediatric patients with relapsed/refractory solid tumors. A model-informed approach supported a starting dose of 28 mg/m² for patients ≥1 year old, representing 80% of the adult dose. An adult PBPK model, adapted for pediatric patients, predicted that this dose would achieve comparable exposures to the approved adult dose of 60 mg. Clinical pharmacokinetic data from a Phase I study confirmed that the 28 mg/m² dose provided consistent exposures across the pediatric age range. This approach successfully validated the pediatric dose recommendation for copanlisib.
Traver E, et al. [69], 2024	Web of Science	Leriglitazone	CNS diseases	SimCYP® version 17	This study assessed leriglitazone, a PPARγ agonist, for its pharmacokinetics and CNS efficacy in neurodegenerative diseases. A Phase 1 trial in healthy male volunteers, involving single and multiple ascending doses, showed that leriglitazone is rapidly absorbed with no food effect and has a linear dose-exposure relationship. A PBPK model was developed using Phase 1 data, incorporating CYP3A4 and CYP2C8 metabolism and biliary clearance. The model successfully predicted pediatric doses, which were preliminarily verified in five pediatric patients.
Tsamandouras N, et al. [70], 2015	Web of Science	Simvastatin	Cardiovascular disease	SimCYP® version 13	This study developed a population PBPK model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), to predict their concentrations in liver (efficacy) and muscle (toxicity). Plasma concentrations from 34 healthy volunteers were analyzed using a mechanistic model that incorporates SV/SVA inter-conversion in different tissues. The model successfully described SV/SVA plasma data and predicted the effects of OATP1B1 polymorphism and drug-drug interactions on concentrations. It also aligned with observed clinical efficacy and toxicity outcomes, supporting its use in assessing drug interactions and myopathy risk.
Salerno S, et al. [71], 2017	Cochrane Library	Solithromycin	Antibiotic	PK-Sim and MoBi version 6.2	This study developed a whole-body PBPK model for solithromycin in adults using PK-Sim and MoBi, incorporating time-dependent CYP3A4 auto-inhibition. Plasma and epithelial lining fluid (ELF) concentration data from 100 healthy subjects and 22 patients with community-acquired bacterial pneumonia (CABP) were used for model evaluation. Population simulations showed that 11% and 23% of observations fell outside the 90% prediction interval for plasma and ELF, respectively. The oral regimen (800 mg on day 1, 400 mg daily on days 2–5) was predicted to be effective, with ≥97% of simulated adults achieving the target AUC/MIC ratios for ELF.
Venuto C, et al. [72], 2020	Cochrane Library	Nilotinib	Anticancer	SimCYP®	This study aims to develop a PBPK model to predict nilotinib concentrations in plasma and cerebrospinal fluid (CSF) and compare the predictions to observed data from the NILO-PD clinical trial. Nilotinib, a c-Abl inhibitor, was investigated in patients with Parkinson’s disease. Serum and CSF concentrations were measured, showing low CSF-to-serum ratios (0.002-0.003) for the 150mg and 300mg doses. Using the Simcyp Simulator, a whole-body PBPK model will simulate nilotinib pharmacokinetics in serum, CSF, and brain and compare the results to clinical trial data to validate the model’s accuracy.
Rhee SJ, et al. [73], 2018	Cochrane Library	Fimasartan, Amlodipine, and Hydrochlorothiazide	Cardiovascular disease	SimCYP® version 15	This study aimed to develop a PBPK model for fimasartan, amlodipine, and hydrochlorothiazide and assess DDI potentials. Using Simcyp software, the PBPK model was constructed with data from literature and in vitro studies and validated by comparing predicted pharmacokinetics with observed data in healthy subjects. The model predicted no significant DDI for co-administration of fimasartan with amlodipine or hydrochlorothiazide, which is consistent with clinical observations. The simulation at steady-state showed a 24.5% increase in fimasartan exposure with no changes in amlodipine and hydrochlorothiazide exposures. The model effectively predicts DDI potential.
Hwang S, et al. [74], 2024	Cochrane Library	Methotrexate	Antirheumatic	SimCYP® version 21	This study developed a robust PBPK model to quantitatively assess drug-drug interactions (DDIs) for methotrexate (MTX) mediated by transporters, demonstrating that co-administration with rifampicin and febuxostat increased MTX systemic exposure by 33% and 17%, respectively, and by 52% when combined, validating the model’s predictive capability for transporter-mediated DDIs.
Samant TS, et al. [75], 2020	Cochrane Library	Ribociclib	Anticancer	SimCYP® version 18	This study developed a PBPK model for ribociclib, used in combination with endocrine therapy for HR-positive and HER2-negative advanced breast cancer. The model integrated in vitro, preclinical, and clinical data. Key findings included ritonavir increasing ribociclib’s AUC by 3.2-fold, while rifampin decreased it by 89%. Additionally, ribociclib raised midazolam’s AUC by 3.8-fold and caffeine’s by 1.2-fold. Predictions showed that multiple ribociclib doses could increase midazolam AUC by 5.85-fold in cancer patients, with ritonavir increasing ribociclib AUC by 1.31-fold. The study recommends avoiding strong CYP3A inhibitors or inducers and exercising caution with CYP3A substrates with narrow therapeutic indices.
Chen B, et al. [76], 2022	Cochrane Library	Acalabrutinib	Anticancer	SimCYP® version 19	This study aimed to evaluate the pharmacokinetic interactions between acalabrutinib and moderate CYP3A inhibitors, fluconazole and isavuconazole, using both experimental data and a PBPK model. The effect on acalabrutinib and its active metabolite, ACP-5862, was investigated in an open-label, randomized, 2-period study. Co-administration with fluconazole and isavuconazole increased acalabrutinib’s maximum plasma concentration and area under the curve, while reducing ACP-5862 exposure. The PBPK model accurately predicted these PK profiles. There were minimal safety concerns, and no dose adjustments were deemed necessary for co-administration with moderate CYP3A inhibitors.
Djebli N, et al. [77], 2015	Cochrane Library	Clopidogrel	Antiplatelet	SimCYP® version 10.2	This study developed and validated a dynamic PBPK model in Simcyp for clopidogrel and its active metabolite clopi-H4 across four CYP2C19 phenotypic populations, accurately predicting the area under the curve (AUC) values for each group and demonstrating reliable predictions of pharmacokinetics and drug-drug interactions, making it the first model to simultaneously predict the pharmacokinetics of a prodrug and its metabolites based on genetic variability in metabolizing enzymes.
Xiao Q, et al. [78], 2015	Cochrane Library	Repaglinide and pioglitazone	Antidiabetic	SimCYP® version 14.1	This study investigated the inhibitory effect of repaglinide on pioglitazone metabolism using in vitro, in silico, and in vivo methods. In vitro studies demonstrated a strong inhibitory effect of repaglinide (Ki = 0.0757 µM, [I]/Ki > 0.1) on pioglitazone metabolism, while an IVIVE-PBPK model using Simcyp® predicted AUC and Cmax ratios of approximately 1.01 between treatment groups. However, clinical trials with 12 healthy volunteers revealed no significant difference in pioglitazone pharmacokinetics (p > 0.05) when coadministered with repaglinide. This discrepancy was attributed to extensive plasma protein binding and high clearance of repaglinide, leading to lower in vivo concentrations compared to in vitro conditions.
Chen J, et al. [79], 2022	Cochrane Library	Salvianolic acid A	Cardiovascular disease	GastroPlus® Version 9.8	This study developed a PBPK model that successfully predicted the pharmacokinetics of salvianolic acid A (SAA), particularly its plasma concentrations, in healthy subjects. The results demonstrated a lack of dose proportionality after single doses, with the area under the curve (AUC₀₋ₜ) showing higher-than-expected increases at higher doses. Specifically, the 90% confidence intervals for the slope of AUC₀₋ₜ (1.222 [1.156-1.288]) exceeded the predefined bioequivalence range, suggesting saturation of transport mechanisms such as hepatic OATP1B1 and P-glycoprotein (P-gp) at higher doses. These findings highlight potential challenges in achieving dose proportionality in SAA pharmacokinetics, which the PBPK model was able to predict and simulate accurately.
Kaur N, et al. [80], 2020	Cochrane Library	Irbesartan	Cardiovascular disease	GastroPlus™	This study mechanistically examined the oral absorption behavior of the weakly basic drug irbesartan (IRB) by investigating its pH-dependent solubility, supersaturation, and precipitation patterns. Initial simulations using equilibrium solubility were inadequate for accurately predicting oral absorption. However, the use of a multi-compartment biorelevant dissolution testing model, simulating conditions in the stomach and duodenum, allowed for sustained intestinal supersaturation (2-4-fold) across varying gastric-to-intestinal transfer rates. When combined with dissolution data, GastroPlus™ simulations predicted plasma exposure with greater accuracy (within ± 15% prediction error). The study found that amorphous precipitate formation with significant particle size reduction (about 10-fold) contributed to maintaining intestinal supersaturation, improving oral pharmacokinetics predictions for irbesartan.

4. Discussion

5. Conclusions

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