Table 1 shows the major application areas of successful PBPK submissions to the FDA that resulted in a study waiver or labelling recommendations, both for the investigational drug as a perpetrator of DDIs and as a victim of DDIs. These cases cover drug interactions mediated by CYP2C8, CYP2C9, CYP3A and organic anion transporter 3 (OAT3). The first application under “investigational drug as perpetrator of reversible inhibition and/or induction of CYPs and/or transporter inhibition” is the prediction of a lack of clinical DDI potential, if the model-predicted AUCR with an index substrate under ‘worst-case’ conditions has been confirmed to be within the range of 0.8–1.25. To account for the uncertainty in the key model parameters, fraction unbound in the in vitro incubation (fu,inc) and inhibition constant (Ki), a sensitivity analysis is carried out, by varying these parameters over a reasonable range. A ten-fold range is generally recommended for Ki, reflecting the large inter-laboratory variations in the in vitro assays. No difference in outcome is anticipated between MSM and PBPK for this category, as this is only a screening exercise and both methods are expected to predict a lack of relevant interaction. Therefore, no specific case is chosen for this category. Similarly, no specific case is chosen for the prediction of effect of CYP modulators on sensitive substrates under “investigational drug as victim of CYP inhibition, CYP induction, or transporter inhibition”. Here, a drug is considered a sensitive substrate based on absorption, distribution, metabolism and elimination (ADME)/mass balance studies. A prediction to support conservative labelling to avoid concomitant use of strong inhibitors and inducers cannot be different between MSMs and PBPK models for this class, as this is dependent on the fm,CYP derived from a mass balance study combined with either in vitro recombinant CYP kinetics or an in vitro chemical inhibition study. For all other categories of major applications of PBPK models for an investigational drug as a victim or perpetrator of DDIs, we use representative examples to compare the performance of MSMs and PBPK models. The results from MSMs are presented alongside those reported for PBPK modelling in Sects. 3.1–3.7. The IndC50 for rifampin at a hepatic level derived from its interaction with midazolam administered intravenously [16] is 1.9 µM.

Table 1 Application areas of successful physiologically based pharmacokinetics submissions to the US Food and Drug Administration that resulted in a study waiver or labeling recommendations and representative examples in each shown within brackets3.1 Voxelotor (CYP3A4 Inhibitor)

To evaluate the effect of voxelotor, a CYP3A modulator on the pharmacokinetics of midazolam, a sensitive CYP3A substrate at the higher registrational dose of voxelotor, after the model confirms the clinical DDI effect observed at a different dosing regime.

3.1.1 Background

In an in vivo cocktail DDI study [19], voxelotor was administered at a dose of 900 mg on days 1 and 2, and at a dose of 600 mg on day 3 through day 7. The cocktail was administered on day 4. In this study, voxelotor altered the exposure of only midazolam (a CYP3A substrate) by 63% (AUCR = 1.63), but not the exposure of other sensitive CYP substrates [19].

3.1.2 PBPK Model Predictions

Physiologically based pharmacokinetic model predictions of DDI effects at the registrational dose of 1500 mg once daily (QD) were performed by the sponsor, following optimisation of voxelotor Ki using the midazolam interaction in a cocktail study. The predictions led to a conservative label recommendation in Sect. 7.2 to avoid coadministration of voxelotor with sensitive CYP3A4 substrates with a narrow therapeutic index or if concomitant use is unavoidable, to consider a dose reduction of the sensitive CYP3A4 substrate(s) [19].

3.1.3 MSM

Mechanistic static models were used in this study to evaluate the effect of voxelotor as a perpetrator at the registrational dose level of 1500 mg QD, after the model confirmed the results of the cocktail study. The input data used in the model are shown in Table 2.

Table 2 Input parameters for voxelotor3.1.4 Parameter Optimisation

As in the PBPK analysis, the DDI effect of the voxelotor–midazolam interaction observed in the cocktail study was used to optimise voxelotor Ki for CYP3A inhibition. While voxelotor Ki is the only model parameter requiring optimisation in PBPK modelling, the fraction of midazolam escaping gut metabolism (fgut,i) following inhibition by voxelotor is also unknown in MSMs. To overcome this challenge, all possible combinations of voxelotor Ki and midazolam fgut,i were explored that could recover the observed AUCR of 1.63 for the voxelotor–midazolam interaction.

All possible parameter combinations in two extreme scenarios have been considered to predict DDIs at the clinically tested dosing regime of 900 mg on days 1 and 2, and at a dose of 600 mg on day 3 through day 7. One, where only gut interaction and no systemic interaction occurs (scenario 1) and another, where only a systemic interaction and no gut interaction occurs (scenario 2). To consider scenario 1, fgut,i of midazolam had to be reduced to at least 0.87 (if there was complete inhibition of gut enzymes by voxelotor, fgut,i for midazolam would be 1). In addition, Ki must be 0.5 µM or more to ensure the absence of a systemic interaction to match the predicted DDI to the clinically observed value of 1.63 (see Table 3). To consider scenario 2, fgut,i for midazolam is set to its fgut (0.57) [21], so that the fraction escaping gut metabolism with and without voxelotor is the same. The voxelotor Ki that recovers the observed AUCR of 1.63 for voxelotor-midazolam interaction is then 0.06 µM (see Table 3). In both scenarios, the inhibitor (voxelotor) concentration was set to the average plasma concentration on day 4 in the cocktail study at 900/600 mg (see Table 2).

Table 3 Mechanistic static model-predicted AUCR of midazolam considering all possible parameter combinations of voxelotor Ki and midazolam fgut,i

The MSM was then used to estimate the AUCR of midazolam at the registrational dose of voxelotor (1500 mg). The steady-state concentration of voxelotor at 1500 mg was used in the model (see Table 2) using both sets of parameters. The results are shown in Table 3. The MSM predicted AUCR at 1500 mg of voxelotor compares reasonably well with PBPK predictions (see Table 4).

Table 4 Comparison of MSM- and PBPK-predicted AUCR of midazolam

Thus, the MSM could only predict a range of plausible values for a midazolam interaction with voxelotor 1500 mg QD. However, given the very low fup of voxelotor (see Table 2), scenario 1 (AUCR = 1.78) is more likely.

3.2 Ivosidenib (CYP3A Inducer)

To predict the effect of CYP3A induction on midazolam by using an autoinduction effect of ivosidenib (a sensitive CYP3A substrate), in lieu of a DDI study with midazolam and to evaluate the effects of rifampin (CYP3A) on the steady-state exposure of ivosidenib.

3.2.1 Background

Ivosidenib, an isocitrate dehydrogenase-1 inhibitor to treat acute myeloid leukaemia (AML) in patients with a susceptible isocitrate dehydrogenase-1 mutation, is a substrate of P-glycoproteein (P-gp) and primarily metabolised by CYP3A (98%). It is also an inducer of CYP3A4, CYP2B6, CYP2C8 and CYP2C9 [22]. As a substrate and inducer of CYP3A, auto-induction effects of ivosidenib observed in repeated-dose clinical pharmacokinetic profiles at various doses could be used to characterise the induction potential in lieu of DDI study with a sensitive CYP3A substrate such as midazolam. In the PBPK model, IndC50 was derived through a sensitivity analysis to compare the simulated ratios of maximum concentration and AUC0–24 of ivosidenib at steady state versus a single dose over a range of CYP3A4 induction parameters. The value of IndC50 that led to a prediction of accumulation that best matched with the clinically observed accumulation was identified as the IndC50 [22].

3.2.2 PBPK Model Predictions

The PBPK model predictions of the effect of co-administration of ivosidenib (500 mg QD for 19 days) on midazolam (5 mg on day 15) led to the label recommendation to avoid concomitant use of ivosidenib with sensitive CYP3A substrates [22]. Model predictions of the effect of concomitant rifampin (600 mg QD for 15 days) on the steady-state exposure of ivosidenib (500 mg QD for 30 days) led to the conservative label recommendation to avoid coadministration of strong CYP3A inducers.

The inputs used in the MSM to evaluate the effect of CYP3A induction by ivosidenib at 500 mg QD on midazolam and to evaluate the effects of CYP3A inducer rifampin on steady-state exposure of ivosidenib are shown in Table 5. To evaluate the effect of CYP3A induction by rifampin alone on the steady-state exposure of ivosidenib, the combined effect of rifampin and ivosidenib on the steady-state exposure of ivosidenib is first determined by additive and multiplicative models (see ESM) and the average calculated. The fold induction due to rifampin alone is calculated as the ratio of reduction in the exposure of ivosidenib due to the combined effect to the reduction in exposure of ivosidenib due to the autoinduction effect.

Table 5 Input parameters for ivosidenib3.2.3 IndC50 Optimisation

As in the PBPK analysis, IndC50 was optimised in MSM, by identifying the value of the parameter needed to recover the observed AUCR of ivosidenib autoinduction, by using ivosidenib victim and perpetrator parameters (Table 5) in the MSM for induction. The AUCR of ivosidenib autoinduction is simply the inverse of the steady-state fold change in the apparent clearance of ivosidenib due to the autoinduction effect of ivosidenib in patients with AML, which was estimated by population pharmacokinetic analyses to be 1.66 [22]. By systematically varying IndC50 value used in the MSM equation for induction, the value leading to a predicted AUCR that matches the observed AUCR of autoinduction is identified as the optimised IndC50. This approach is equivalent to the sensitivity analysis in PBPK modelling described in Sect. 3.2.1.

3.2.4 Results

The MSM predictions of the effect of co-administration of ivosidenib (500 mg QD for 19 days) and midazolam (5 mg on day 15) as well as predictions of the effect of concomitant rifampin (600 mg QD for 15 days) on the steady-state exposure of ivosidenib (500 mg QD for 30 days) are somewhat comparable to those from PBPK modelling (Table 6).

Table 6 Comparison of MSM- and PBPK-predicted induction effects of ivosidenib as a perpetrator with midazolam as a victim with rifampin

Fold change in ivosidenib clearance due to rifampin alone = fold change in ivosidenib clearance due to combined effect/fold change in ivosidenib clearance due to autoinduction effect (see Table 7)

Table 7 Induction effect of ivosidenib with or without rifampin on the exposure of ivosidenib3.3 Ibrutinib (CYP3A Substrate)

To predict the effect of moderate (erythromycin, diltiazem) and weak (fluvoxamine) CYP3A inhibitors and moderate CYP3A inducer (efavirenz) on ibrutinib exposure, after the model has been confirmed with a strong CYP3A inhibitor (ketoconazole) and an inducer (rifampin).

3.3.1 Background

Ibrutinib is a potent covalent inhibitor of Bruton’s tyrosine kinase developed for treating B-lymphocyte cell malignancies and is approved for relapsed chronic lymphocytic leukemia and mantle cell lymphoma [23]. The primary route of elimination of ibrutinib is through CYP3A4-mediated metabolism [24]. Co-administration of ibrutinib 40 mg with ketoconazole (400 mg QD) increased ibrutinib AUC by 24-fold, while 560 mg co-administration of ibrutinib with rifampin (600 mg QD) decreased ibrutinib AUC by ten-fold [25].

3.3.2 PBPK Analysis

A PBPK model was developed for ibrutinib by the sponsor based on available physicochemical properties, in vitro experiments and clinical pharmacokinetic parameters [26]. Models for all modulators were from the Simcyp compound library. The DDI of ketoconazole 400 mg QD with ibrutinib was simulated assuming complete inhibition of intestinal CYP3A4 by setting the fraction unbound escaping the intestine (fu,gut) for ibrutinib to 1. The Ki of ketoconazole was optimised (to 2.43 µM) to recover the clinical interaction with ibrutinib (AUCR = 24). Fluvoxamine inhibition potency towards CYP2C19 in vivo is reported to be ∼40 times greater than in vitro [24]. To cover a potential underprediction of the in vitro CYP3A Ki in the fluvoxamine model, the fluvoxamine in vivo CYP3A4 Ki specific for ibrutinib inhibition was determined by adjusting the in vitro Ki of fluvoxamine with ibrutinib by the same factor that was needed to recover the in vivo DDIs with midazolam (Ki = 2.6 μM) or alprazolam (Ki = 0.52 μM), This was possible, although fluvoxamine is known to extensively bind to protein in in vitro models, as the in vitro conditions for all three compounds (midazolam, alprazolam and ibrutinib) were maintained at similar protein concentrations. This strategy has been used and discussed in PBPK models using other Software platforms [27]. The fluvoxamine Simcyp file with the optimised ibrutinib specific Ki (3 μM) was used to predict the fluvoxamine–ibrutinib interaction. For all other modulators, the models in Simcyp V14 were adopted as such. Based on the PBPK predictions, the label for ibrutinib recommends that co-administration of ibrutinib with strong inhibitors or inducers should be avoided. Ibrutinib dose should be reduced to 140 mg (quarter of maximal prescribed dose) when co-administered with moderate CYP3A4 inhibitors so that its exposures remain within observed ranges at therapeutic doses. The label does not specify any recommendation for concomitant moderate inducers.

3.3.3 MSMs

Mechanistic static models were used in this study to evaluate the DDI effects of modulators on ibrutinib with fm = 1, fm,CYP3A =1 and assuming complete inhibition of intestinal CYP3A by ketoconazole (fgut,i = 1). Unlike PBPK simulations, Ki of ketoconazole was not optimised using the ketoconazole-ibrutinib clinical DDI study. Instead, this DDI study was used to optimise the only unknown victim-related parameter in the model, which is fgut of ibrutinib. Optimisation of ibrutinib fgut was achieved by systematically varying its value, until model prediction of ibrutinib AUCR matched the observed ibrutinib AUCR. The optimised value of fgut is 0.345. In a further deviation from the PBPK analysis, the fgut was validated using the clinical DDI study of ibrutinib with itraconazole [42]. Ibrutinib is the only drug in this study, for which a deviation from the PBPK workflow was necessary.

3.3.4 Results

The mechanistic model predictions are not very comparable to those obtained from PBPK models (see Table 8) for induction, possibly owing to the different approaches used in the two models to optimise model parameters based on a clinical DDI study where ketoconazole Ki was optimised in PBPK while fgut was optimised in the MSM). Another reason could be that the interaction parameters for modulators were from Simcyp V19 in the MSM, whereas the submission to the FDA was done with an older version of Simcyp. However, in the absence of a clinical study with efavirenz, it is difficult to understand the differences between the two approaches.

Table 8 Comparison of MSM- and PBPK-predicted AUCR of inhibitors and inducers on the exposure of ibrutinib3.4 Voxelotor (CYP3A Substrate)

Evaluate the effects of CYP3A inhibitors (ketoconazole, fluconazole) and inducers (efavirenz, rifampin) on the pharmacokinetics of voxelotor using an estimated contribution of CYP3A4 to the total drug metabolism (fmCYP3A4) of 0.36 or 0.56, derived from mass balance [19] with either in vitro recombinant CYP kinetics or the in vitro chemical inhibition study, respectively. Evaluate the DDI effects considering a worst-case scenario attributing all oxidative metabolism of voxelotor to CYP3A (fmCYP3A4 = 0.74) [19].

3.4.1 Background

Voxelotor undergoes oxidative metabolism mediated by multiple CYPs (3A4, 3A5, 2C9, 2C19 and 2B6) (73.78%), direct conjugation by UGT1A1 and UGT1A9 (18.7%) as well as reduction (7.52%) [19]. Cytochrome P450 3A accounts for 36–56% of metabolism while the fraction metabolised by other individual CYPs is less than 10%. Given its very low metabolic clearance 3.35 L/h [19], it is unlikely to be affected by intestinal CYP3A-mediated metabolism or drug interactions in the gut [19].

3.4.2 PBPK Analysis

Even in the absence of a dedicated DDI study to evaluate the effect of a CYP3A modulator on the pharmacokinetics of voxelotor, the results of the PBPK evaluation [19] and pharmacokinetic/pharmacodynamic analysis were considered sufficient to recommend a dose adjustment in the US prescribing information Sects. 2, 7, 12.3 for voxelotor when it is co-administered with CYP3A modulators.

3.4.3 MSM Calculations

Mechanistic static model calculations were performed using the models shown in the ESM, using the same data that were employed in the PBPK analysis reported [19]. The DDI effects of CYP3A inhibitors (ketoconazole, fluconazole) and inducers (efavirenz, rifampin) on voxelotor exposure were done using an estimated contribution of CYP3A4 to the total drug metabolism (fmCYP3A4) of 0.36 or 0.56 and for the worst-case scenario, attributing all oxidative metabolism of voxelotor to CYP3A. To consider the low likelihood of intestinal CYP3A-mediated drug interactions, fgut,i and fgut were set to 1 (assuming that the fraction of voxelotor escaping intestinal metabolism in the presence and absence of inhibitor is the same). In the case of fluconazole and fluvoxamine that inhibit both CYP3A4 and CYP2C9, MSM predictions were done using Ki for each enzyme separately. The two resulting AUCRs are multiplied to get the overall AUCR, which is then compared to the PBPK model. As with the PBPK analysis, no parameter optimisation was done.

3.4.4 Results

The MSM predictions are comparable to those obtained from PBPK modelling for the modulators evaluated (see Table 9). It is worth noting that a clinical DDI study of voxelotor with itraconazole conducted later showed only a modest 11% increase of voxelotor AUC in healthy subjects according to US product labelling [45]. This modest effect may be explained based on the multiple metabolic pathways and the absence of a gut interaction for voxelotor.

Table 9 Comparison of mechanistic static model/physiologically based pharmacokinetic-predicted ratio of the area under the plasma concentration–time curve of CYP inhibitors and inducers on the exposure of voxelotor in healthy subjects3.5 Siponimod (Substrate of CYP3A4 and CYP2C9)

To predict DDI effects of CYP modulators (CYP3A4/CYP2C9 inhibitors or inducers) on the exposures of siponimod in sub-populations carrying polymorphic CYP2C9 variants.

3.5.1 Background

Siponimod is a sphingosine 1-phosphate receptor modulator indicated for the treatment of relapsing forms of multiple sclerosis. [28, 29] The absolute bioavailability of siponimod is approximately 84%, and the mean elimination half-life is 30 hours, respectively. The oral clearance of siponimod is very low. Siponimod is a substrate of both CYP2C9 and CYP3A4 with fm,CYP of 80% and 18%, respectively, by each enzyme. It is not a modulator of CYP enzymes and is not a substrate of uptake or efflux transporters.

3.5.2 PBPK Analyses [28, 29]

The PBPK analyses [28, 29] were performed by the sponsor to evaluate the effects of CYP3A4/CYP2C9 modulators on siponimod pharmacokinetics in subjects with various CYP2C9 genotypes. The model was initially validated by comparing PBPK predictions with observed pharmacokinetic data in subjects carrying CYP2C9 *1/*1, *2/*3 and *3/*3. The PBPK model was also able to recover the DDI when siponimod was co-administered with fluconazole (a CYP2C9 and CYP3A inhibitor), itraconazole (a CYP3A inhibitor) and rifampin (a CYP2C9 and CYP3A inducer) in subjects carrying CYP2C9 genotypes *1/*1, *1/*2 or 1/*3. It was then used to predict the DDI effects of CYP2C9 and CYP3A modulators (fluconazole, ketoconazole, erythromycin, fluvoxamine, and efavirenz) on the pharmacokinetics of siponimod in subjects carrying CYP2C9 *1/*1, *1/*2, *2/*2, *1/*3 and *2/*3 genotypes.

The PBPK predictions along with genomic and DDI studies led to recommendations to avoid concomitant administration of a moderate CYP2C9/3A4 dual inhibitor or a moderate CYP2C9 inhibitor with a strong or moderate CYP3A4 inhibitor for all patients; avoid concomitant use of strong CYP3A4/moderate CYP2C9 inducers (e.g. rifampicin or carbamazepine) in all patients; and avoid concomitant use of moderate CYP3A4 inducer with siponimod in patients with CYP2C9 *1/*3 or *2/*3 genotypes.

3.5.3 MSM

Mechanistic static models were used in this study to evaluate the effects of CYP2C9 and CYP3A modulators (fluconazole, ketoconazole, erythromycin, fluvoxamine, rifampin and efavirenz) on the pharmacokinetics of siponimod in subjects carrying CYP2C9 *1/*1, *1/*2, *2/*2, *1/*3, and *2/*3 genotypes. First, fm,CYP2C9 and fm,CYP3A4 are estimated for each of the genotypes as shown in Table 10. Clearance driven by CYP2C9 in each genotype is calculated by subtracting the fractional clearance due to all other enzymes (assumed constant in all genotypes) from total clearance in that genotype. Contribution of CYP3A4 and all other enzymes is calculated as 20% of total clearance in *1/*1. To consider the low likelihood of intestinal CYP3A-mediated drug interactions for siponimod because of its low clearance, fgut,i and fgut were set to 1 (fraction of siponimod escaping intestinal metabolism in the presence and absence of inhibitor is the same). Fluconazole and fluvoxamine inhibit both CYP3A and CYP2C9. In MSMs, the Ki of each enzyme is separately used in MSMs to arrive at an AUCR for each enzyme. The individual AUCR values are then multiplied to give the overall AUCR to consider the inhibition of both enzymes.

Table 10 Calculation of fmCYP2C9 and fmCYP3A4 of siponimod by genotype [29]3.5.4 Results

Table 11 summarises AUCRs predicted by an MSM. Mechanistic static model predictions of siponimod interactions with inhibitors and efavirenz were comparable to those from the PBPK model. However, MSM predictions of siponimod interactions with rifampin showed a higher risk relative to predictions from PBPK modeling. In a dedicated DDI study with rifampin 600 mg once daily, the AUC of siponimod was reduced by 57% (AUCR ~0.43) [29]. Thus, PBPK predictions are closer to the observation. Deviations were also higher for the *2/*2 genotype. For this genotype, the interaction risk predicted by PBPK modeling appears to be higher than in the *1/*1 genotype, which is difficult to understand considering fm,CYP2C9 in the two genotypes.

Table 11 Comparison of MSM- and PBPK-predicted AUCR of CYP inhibitors and inducers on the exposure of siponimod in healthy subjects3.6 Apalutamide (Substrate of CYP3A and CYP2C8)

To predict the effects of CYP3A and CYP2C8 modulators on the steady-state pharmacokinetics of apalutamide and its active metabolite, N-desmethyl apalutamide (NAPA).

3.6.1 Background

Apalutamide is a selective androgen receptor inhibitor for the treatment of prostate cancer. It is metabolised by CYP2C8 and CYP3A4 to form an active metabolite, NAPA. In vitro, it is a CYP2C8 inhibitor, CYP3A inducer, and an inhibitor of other enzymes and transporters (CYP2C19 and OCT2, multidrug and toxin extrusion proteins, and OAT3). In vivo, apalutamide showed dose-proportional increases in exposure across the dose range of 30–480 mg following both single and repeated doses [30]. Itraconazole did not change the AUC of apalutamide and NAPA; gemfibrozil increased the AUC0–672 and AUCinf of apalutamide by 53% and 68%, respectively, and decreased the area AUC0–672 and AUCinf of NAPA by 43% and 15%, respectively, following a single-dose administration of apalutamide. In vivo, multiple daily doses of apalutamide decreased the AUC of the CYP3A substrate midazolam and CYP2C8 substrate pioglitazone (a) by 92% and 18%, respectively [31, 32]. As a substrate and inducer of both CYP3A and CYP2C8, apalutamide has a higher clearance at steady state. The apparent clearance of apalutamide was estimated by population pharmacokinetics to be 1.31 L/h after single dosing and increased to 2.04 L/h at steady state after QD dosing, resulting in an AUCR of 0.64 due to autoinduction [30]. As the clinical DDI studies were done with single-dose apalutamide, the goal of the PBPK analysis was to assess the impact of CYP inhibition and induction on the steady-state exposure of apalutamide.

3.6.2 PBPK Models

The PBPK models of apalutamide and NAPA were developed by sponsor using data from in vitro, human mass balance, pharmacokinetic, and multiple clinical DDI studies. The model was used to simulate untested DDI scenarios of a CYP2C8 inhibition by gemfibrozil, CYP3A inhibition by ketoconazole and CYP induction by rifampin on the pharmacokinetics of apalutamide and its active metabolite, NAPA, at steady state, after the model confirmed the clinical interactions following a single dose of apalutamide. As the exposure of ketoconazole was significantly decreased by rifampin, it is likely to be affected by multiple-dose administration of apalutamide (also, a strong CYP3A inducer) [31]. The PBPK model therefore simulated two different scenarios of an apalutamide–ketoconazole interaction, depending on whether ketoconazole exposure was affected (real-world scenario) or unaffected (worst-case scenario mimicking a strong CYP3A inhibitor that is not a CYP3A substrate) by apalutamide [30,31,32]. The untested combined induction effect of rifampin and apalutamide on the steady-state pharmacokinetics of apalutamide, evaluated with multiplicative and additive induction models, showed that the combined induction effect is not very different to the autoinduction of apalutamide. Based on the modelling results, the FDA label does not recommend any dose adjustments with concomitant administration of apalutamide and strong CYP3A or CYP2C8 inhibitors or with CYP3A/CYP2C8 inducers as the combined exposure of apalutamide and NAPA may not significantly change.

3.6.3 MSM

Mechanistic static models were used to (1) evaluate the effects of gemfibrozil and ketoconazole (in both real-world and worst-case conditions) on the pharmacokinetics of apalutamide and NAPA at steady state, after model-confirmed clinical interactions of apalutamide single dose with gemfibrozil and itraconazole; and (2) to evaluate the combined CYP3A inducer effects of apalutamide and rifampin on the steady-state exposure of apalutamide and NAPA by multiplicative and additive models. The input data used in the model are presented in Table 12.

Table 12 Input parameters for apalutamide3.6.4 Other Input Parameters

The induction parameters (maximum induction, Indmax and IndC50) of CYP2C8 by rifampin were obtained from Simcyp V19 (6.7 and 0.3 µM, respectively).

3.6.5 Methods3.6.5.1 Calculation of Altered fm,CYP3A and fm,CYP2C8 at Steady State of Apalutamide Due to CYP3A Induction and CYP2C8 Net Induction by Apalutamide

The fm,CYP2C8 of CYP2C8 substrate pioglitazone, derived from clinical DDI studies with trimethoprim and gemfibrozil, are 0.6 and 0.73 respectively (average = 0.67). Apalutamide has a net induction effect on CYP2C8 at steady state and decreases the AUC of pioglitazone by 18%. This 18% decrease in exposure corresponds to a fold increase in the total clearance of pioglitazone of 1/(1–0.18) = 1.22-fold, due to CYP2C8 induction by apalutamide. Assuming this fold increase in total clearance is all due to CYP2C8 induction, the fractional CYP2C8 clearance of pioglitazone following induction by apalutamide = CLpio × 1.22 − CLpio × (1–0.67) = CLpio × 0.89, where CLpio is the clearance of pioglitazone in the absence of apalutamide. Thus, the fold induction of CYP2C8 is (CLpio × 0.89)/(CLpio × 0.67) = 1.33. The fractional CYP2C8 clearance of apalutamide at SS = 1.33 × fractional CYP2C8 clearance after single dose = 1.33 × 0.56 = 0.74 L/h. Subtracting the increase in CYP2C8 clearance from the total increase in clearance stemming from autoinduction of both CYP3A and CYP2C8 (0.73 L/h), the increase in apalutamide clearance at steady state due to CYP3A4 induction alone can be estimated. fm,CYP2C8,SS and fm,CYP3A,SS are calculated by dividing the fractional clearance of CYP2C8 and CYP3A at steady state (fCL2C8,SS and fCL3A4,SS) by the total clearance at steady state. Thus,

Instead of using the pioglitazone–apalutamide DDI for the calculation of fm,CYP2C8,SS and fm,CYP3A4,SS as above, the midazolam–apalutamide DDI could have been used. This requires deconvolution of the gut contribution of apalutamide induction from the systemic contribution (because apalutamide will only have the systemic interaction), which would add to the uncertainty. Results are shown in Table 13.

Table 13 SD and SS parameters for apalutamide3.6.5.2 Estimation of Impact of Modulators on the Exposure of Apalutamide

Use perpetrator and victim parameters of apalutamide (Table 12) and interaction parameters of modulators (Table S1 of the ESM) in MSMs to estimate the AUCRs of apalutamide. Detailed workflow and calculations are available in the ESM. Results are shown in Tables 14 and 15.

Table 14 Apalutamide as a victim and perpetrator of CYP induction: comparison of MSM and PBPKTable 15 Apalutamide as a victim of CYP inhibition: comparison of MSM and PBPK3.6.5.3 Estimation Impact of Modulators on the Exposure of the Active Metabolite, NAPA

The NAPA levels are is proportional to the sum of CYP3A- and CYP2C8-mediated clearances as they are formed by the action of both enzymes on apalutamide. The effect of CYP3A4 and CYP2C8 modulators on NAPA levels is estimated by considering the changes in the fractional clearances by the two enzymes brought about by the modulators. The AUCR of NAPA at steady state due to inhibition of CYP3A4-mediated clearance of apalutamide by ketoconazole without incorporating induction of ketoconazole by apalutamide is estimated by recognising that NAPA levels are proportional to the sum of CYP3A- and CYP2C8-mediated clearances (CL2C83A4). Therefore, the AUCR of NAPA due to CYP3A inhibition by ketoconazole is calculated as follows:

$$AUCR\left(NAPA,SS, CYP\,inhibition\right)=\frac\,+\,apalutamide\,fCL2C8,SS}$$

Similarly, the AUCR of NAPA at steady state due to inhibition of the CYP3A4-mediated clearance of apalutamide by ketoconazole incorporating induction of ketoconazole is estimated. The AUCR of NAPA at steady state due to inhibition of CYP2C8-mediated clearance of apalutamide by gemfibrozil is also estimated by suitably adapting the above equation to CYP2C8. Results are presented in Table 16.

Table 16 Comparison of static and dynamic model predictions of inhibitor and induction effects on the exposure of active metabolite NAPA3.7 Baricitinib (Substrate of OAT3)

To predict the effect of OAT3 inhibition by the moderate-to-weak OAT3 inhibitors ibuprofen and diclofenac on baricitinib exposure after model confirmation of a clinical DDI study with a strong OAT3 inhibitor (probenecid).

3.7.1 Background

Baricitinib is a JAK1/2 inhibitor that reversibly inhibits JAK1/2’s JH1 tyrosine kinase domain in the active conformation by acting as an ATP-competitive inhibitor. It is currently approved for the treatment of rheumatoid arthritis [