Differences in Physiologically Based Pharmacokinetic Predictions between Software Platforms Can Be Clinically Relevant: Cases of Levornestrogel and Ethinylestradiol Concentrations with SIMCYP versus PK-Sim from a User Perspective.

Physiologically based pharmacokinetic (PBPK) modeling of drug-drug interactions between suraxavir marboxil and CYP3A4 inhibitors: Quantitative prediction of pharmacokinetic effects on active metabolite GP1707D07

Ruxolitinib is mainly metabolized by cytochrome P450 (CYP) enzymes CYP3A4 and CYP2C9 followed by minor contributions of other hepatic CYP enzymes in vitro. A physiologically based pharmacokinetic (PBPK) model was established to evaluate the changes in the ruxolitinib systemic exposures with co-administration of CYP3A4 and CYP2C9 perpetrators. The fractions metabolized in the liver via oxidation by CYP enzymes (fm,CYP3A4 = 0.75, fm,CYP2C9 = 0.19, and fm,CYPothers = 0.06) for an initial ruxolitinib model based on in vitro data were optimized (0.43, 0.56, and 0.01, respectively) using the observed exposure changes of ruxolitinib (10 mg) with co-administered ketoconazole (200 mg). The reduced amount of fm,CYP3A4 was distributed to fm,CYP2C9. For the initial ruxolitinib model with co-administration of ketoconazole, the area under the curve (AUC) increase of 2.60-fold was over-estimated compared with the respective observation (1.91-fold). With the optimized fm values, the predicted AUC ratio was 1.82. The estimated AUC ratios of ruxolitinib by co-administration of the moderate CYP3A4 inhibitor erythromycin (500 mg) and the strong CYP3A4 inducer rifampicin (600 mg) were within a 20% error compared with the clinically observed values. The PBPK modeling results may provide information on the labeling, i.e. supporting a dose reduction by half for co-administration of strong CYP3A4 inhibitors. Furthermore, an AUC increase of ruxolitinib in the absence or presence of the dual CYP3A4 and CYP2C9 inhibitor fluconazole (100-400 mg) was prospectively estimated to be 1.94- to 4.31-fold. Fluconazole simulation results were used as a basis for ruxolitinib dose adjustment when co-administering perpetrator drugs. A ruxolitinib PBPK model with optimized fm,CYP3A4 and fm,CYP2C9 was established to evaluate victim DDI risks. The previous minimal PBPK model was supported by the FDA for the dose reduction strategy, halving the dose with the concomitant use of strong CYP3A4 inhibitors and dual inhibitors on CYP2C9 and CYP3A4, such as fluconazole at ≤200 mg. Fluconazole simulation results were used as supportive evidence in discussions with the FDA and EMA about ruxolitinib dose adjustment when co-administering perpetrator drugs. Thus, this study demonstrated that PBPK modeling can support characterizing DDI liabilities to inform the drug label and might help reduce the number of clinical DDI studies by simulations of untested scenarios, when a robust PBPK model is established.

Physiologically Based Pharmacokinetic (PBPK) Modeling for Betrixaban and the Impact of P-Glycoprotein Inhibition on Betrixaban Pharmacokinetics

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (Cmax) area under the concentration-time curve (AUC) values and Cmax and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and Cmax ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

GDC-0810 was under development as an oral anti-cancer drug for the treatment of estrogen receptor-positive breast cancer as a single agent or in combination. In vitro data indicated that GDC-0810 is a potent inhibitor of OATP1B1/1B3. To assess clinical risk, a PBPK model was developed to predict the transporter drug-drug interaction (tDDI) between GDC-0810 and pravastatin in human. The PBPK model was constructed in Simcyp® by integrating in vitro and in vivo data for GDC-0810. The prediction of human pharmacokinetics (PK) was verified using GDC-0810 phase I clinical PK data. The Simcyp transporter DDI model was verified using known OATP1B1/1B3 inhibitors (rifampicin, cyclosporine and gemfibrozil) and substrate (pravastatin), prior to using the model to predict GDC-0810 tDDI. The effect of GDC-0810 on pravastatin PK was then predicted based on the proposed clinical scenarios. Sensitivity analysis was conducted on the parameters with uncertainty. The developed PBPK model described the PK profile of GDC-0810 reasonably well. In the tDDI verification, the model reasonably predicted pravastatin tDDI caused by rifampicin and gemfibrozil OATP1B1/3 inhibition but under-predicted tDDI caused by cyclosporine. The effect of GDC-0810 on pravastatin PK was predicted to be low to moderate (pravastatin Cmax ratios 1.01-2.05 and AUC ratio 1.04-2.23). The observed tDDI (Cmax ratio 1.20 and AUC ratio 1.41) was within the range of the predicted values. This work demonstrates an approach using a PBPK model to prospectively assess tDDI caused by a new chemical entity as an OATP1B1/3 uptake transporter inhibitor to assess clinical risk and to support development strategy.

Ivosidenib is a potent, targeted, orally active, small‐molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P‐glycoprotein (P‐gp), organic anion transporting polypeptide‐1B1/1B3 (OATP1B1/1B3), and organic anion transporter‐3 (OAT3) in vitro. A physiologically‐based pharmacokinetic (PK) model was developed to predict drug‐drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β‐hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (Cmax) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P‐gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P‐gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

Summary:(±) Lercanidipine hydrochloride (HCl) (Zanidip) is an antihypertensive drug structurally related to dihydropyridine calcium-entry blockers: lercanidipine is weakly basic (pK′a = 6.83 at 37°C) and highly lipophilic (log P octan-1-ol/water = 6 at 20-25°C). After oral administration of 10-20-mg

Conducting PK studies in pregnant women is challenging. Therefore, we asked if a physiologically-based pharmacokinetic (PBPK) model could be used to predict the disposition in pregnant women of drugs cleared by multiple CYP enzymes. We expanded and verified our previously published pregnancy PBPK model by incorporating hepatic CYP2B6 induction (based on in vitro data), CYP2C9 induction (based on phenytoin PK) and CYP2C19 suppression (based on proguanil PK), into the model. This model accounted for gestational age-dependent changes in maternal physiology and hepatic CYP3A, CYP1A2 and CYP2D6 activity. For verification, the pregnancy-related changes in the disposition of methadone (cleared by CYP2B6, 3A and 2C19) and glyburide (cleared by CYP3A, 2C9 and 2C19) were predicted. Predicted mean post-partum to second trimester (PP : T2 ) ratios of methadone AUC, Cmax and Cmin were 1.9, 1.7 and 2.0, vs. observed values 2.0, 2.0 and 2.6, respectively. Predicted mean post-partum to third trimester (PP : T3 ) ratios of methadone AUC, Cmax and Cmin were 2.1, 2.0 and 2.4, vs. observed values 1.7, 1.7 and 1.8, respectively. Predicted PP : T3 ratios of glyburide AUC, Cmax and Cmin were 2.6, 2.2 and 7.0 vs. observed values 2.1, 2.2 and 3.2, respectively. Our PBPK model integrating prior physiological knowledge, in vitro and in vivo data, allowed successful prediction of methadone and glyburide disposition during pregnancy. We propose this expanded PBPK model can be used to evaluate different dosing scenarios, during pregnancy, of drugs cleared by single or multiple CYP enzymes.

Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, anddevelop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug-drug interactions (DDI). An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators. For a single 250mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration-time curve, to infinity [AUC0-∞]) and 1.04 (maximum plasma concentration [Cmax]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration-time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC0-∞ and Cmax ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, Cmax ratios 0.91 and 0.81, respectively). Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

PurposeTo provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling.MethodsPBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies.ResultsThe developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold.ConclusionsWhole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

The novel Type B gamma-aminobutyric acid (GABAB)-receptor agonist lesogaberan (AZD3355) has been evaluated as an add-on to proton pump inhibitor treatment for gastroesophageal reflux disease, but the effect of food on the bioavailability of this compound has not been assessed. In this openlabel crossover study, healthy males received single 100 mg doses of lesogaberan (oral solution (A) or oral modified release (MR) capsules with a dissolution rate of 50% (B) or 100% (C) over 4 h) with and without food. Blood plasma concentrations of lesogaberan were assessed over 48 h. A log-transformed geometric mean Cmax and AUC ratio within the 90% confidence interval (CI) range (0.80 - 1.25) was defined as excluding a clinically relevant food effect. Overall, 57 subjects completed the study. Only the oral lesogaberan solution had a fed/fasting Cmax ratio outside the 90% CI range (Cmax ratio: 0.76). AUC ratios were within the 90% CI limits for all three lesogaberan formulations. The only substantial change in tmax associated with food intake was observed for the oral solution (1.0 h without food, 1.8 h with food). In conclusion, a clinically relevant food effect could be excluded for the lesogaberan MR formulations, but not for the oral lesogaberan solution.

The clinical pharmacokinetics and in vitro inhibition of digoxin were examined to predict the P-glycoprotein (P-gp) component of drug-drug interactions. Coadministered drugs (co-meds) in clinical trials (N = 123) resulted in a small, <or=100% increase in digoxin pharmacokinetics. Digoxin is likely to show the highest perturbation, via inhibition of P-gp, because of the absence of metabolic clearance. In vitro inhibitory potency data (concentration of inhibitor to inhibit 50% P-gp activity; IC(50)) were generated using Caco-2 cells for 19 P-gp inhibitors. Maximum steady-state inhibitor systemic concentration [I], [I]/IC(50) ratios, hypothetical gut concentration ([I(2)], dose/250 ml), and [I(2)]/IC(50) ratios were calculated to simulate systemic and gut-based interactions and were compared with peak plasma concentration (C(max))(,i,ss)/C(max,ss) and area under the curve (AUC)(i)/AUC ratios from the clinical trials. [I]/IC(50) < 0.1 shows high false-negative rates (24% AUC, 41% C(max)); however, to a limited extent, [I(2)]/IC(50) < 10 is predictive of negative digoxin interaction for AUC, and [I]/IC(50) > 0.1 is predictive of clinical digoxin interactions (AUC and C(max)).

Use of Rabbits for GI Drug Absorption Studies: Relationship between Dissolution rate and Bioavailability of Griseofulvin Tablets

S185. Using surface electromyography to differentiate between generalized tonic-clonic seizures and psychogenic non-epileptic seizures

BackgroundData on the occurrence times of multiple outcomes, reflecting the temporal profile of disease burden/progression, have been used to estimate treatment effects in various recent randomized trials. Most procedures for analyzing these data require specific model assumptions. When the assumptions are not met, the results may be misleading. Robust, model-free procedures for study design and analysis that enable clinically meaningful interpretations are warranted.MethodsFor each treatment group, we constructed and summarized the estimated mean cumulative count of events over time by the area under the curve (AUC), which can be interpreted as the mean total event-free time lost from multiple undesirable outcomes. A higher curve, and resulting larger AUC, implies a worse treatment. The treatment effect is quantified by the ratio and/or difference of AUCs. The timing and occurrence of recurrent heart failure hospitalizations (HFHs) and cardiovascular (CV) death from Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF), comparing sacubitril/valsartan with valsartan, are presented for illustration. We also discuss the design of future studies on the basis of the proposed method.ResultsWith 48 months of follow-up, estimated AUCs, representing the total event-free time lost to HFHs and CV death, were 11.3 and 13.1 event-months for sacubitril/valsartan and valsartan, respectively. The ratio of these AUCs was 0.86 (95% confidence interval, 0.75 to 1.00; P=0.049), a 14% reduction of disease burden favoring combination therapy. A future study, similar to PARAGON-HF, designed using the new proposal would require fewer patients would than a conventional time-to-first-event analysis.ConclusionsThe proposed method is robust and model-free and provides a clinically interpretable, time-scale summary of the treatment effect. (Funded by National Institutes of Health.)