ABSTRACT:Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for "steady-state" and "time-based" approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.

Bococizumab is a humanized monoclonal antibody binding proprotein convertase subtilisin/kexin type 9, which may be a potential therapeutic option for reducing low-density lipoprotein cholesterol (LDL-C) levels in patients with hypercholesterolemia. In this 24-week, multicenter, double-blind, placebo-controlled, dose-ranging study (NCT01592240), subjects with LDL-C levels≥80 mg/dl on stable statin therapy were randomized to Q14 days subcutaneous placebo or bococizumab 50, 100, or 150 mg or Q28 days subcutaneous placebo or bococizumab 200 or 300 mg. Doses of bococizumab were reduced if LDL-C levels persistently decreased to ≤25 mg/dl. The primary end point was the absolute change in LDL-C levels from baseline to week 12 after placebo or bococizumab administration. Continuation of bococizumab administration through to week 24 enabled the collection of safety data over an extended period. Of the 354 subjects randomized, 351 received treatment (placebo [n=100] or bococizumab [n=251]). The most efficacious bococizumab doses were 150 mg Q14 days and 300 mg Q28 days. Compared with placebo, bococizumab 150 mg Q14 days reduced LDL-C at week 12 by 53.4 mg/dl and bococizumab 300 mg Q28 days reduced LDL-C by 44.9 mg/dl; this was despite dose reductions in 32.5% and 34.2% of subjects at week 10 or 8, respectively. Pharmacokinetic/pharmacodynamic model-based simulation assuming no dose reductions predicted that bococizumab would lower LDL-C levels by 72.2 and 55.4 mg/dl, respectively. Adverse events were similar across placebo and bococizumab groups. Few subjects (n=7; 2%) discontinued treatment because of treatment-related adverse events. In conclusion, bococizumab significantly reduced LDL-C across all doses despite dose reductions in many subjects. Model-based simulations predicted greater LDL-C reduction in the absence of bococizumab dose reduction. The Q14 days regimen is being evaluated in phase 3 clinical trials.

The morbidity and mortality associated with impaired͞delayed fracture healing remain high. Our objective was to identify a small nonpeptidyl molecule with the ability to promote fracture healing and prevent malunions. Prostaglandin E2 (PGE2) causes significant increases in bone mass and bone strength when administered systemically or locally to the skeleton. However, due to side effects, PGE2 is an unacceptable therapeutic option for fracture healing. PGE2 mediates its tissue-specific pharmacological activity via four different G protein-coupled receptor subtypes, EP1, -2, -3, and -4. The anabolic action of PGE2 in bone has been linked to an elevated level of cAMP, thereby implicating the EP2 and͞or EP4 receptor subtypes in bone formation. We identified an EP2 selective agonist, CP-533,536, which has the ability to heal canine long bone segmental and fracture model defects without the objectionable side effects of PGE2, suggesting that the EP2 receptor subtype is a major contributor to PGE2's local bone anabolic activity. The potent bone anabolic activity of CP-533,536 offers a therapeutic alternative for the treatment of fractures and bone defects in patients.T he skeleton has the unique ability to repair and heal itself after injury (1, 2). This process is a cascade of synchronized events involving many systemic and local signaling molecules (3). However, in Ϸ10% of cases, fractured bones heal more slowly (malunion) or fail to heal (nonunion), requiring additional costly medical intervention to repair the fracture (4). These malunions and nonunions cause significant patient morbidity, significantly limiting quality of life and increasing healthcare costs. New therapies that could ensure rapid healing of fractures and bone defects would lessen the need for further medical intervention and greatly reduce the morbidity and loss of independence associated with immobilization.The discovery of bone morphogenetic proteins has increased our understanding of the cascade of events that takes place during fracture healing. Several clinical studies demonstrate the capability of these proteins to induce and facilitate this process (5-8). However, the cost effectiveness, degree of clinical benefit, and long-term safety of these therapies have not been fully elucidated. These issues prompted us to identify additional mechanisms and pathways involved in bone formation that could be modulated with a nonpeptidyl small molecule. Such a compound could be used as a therapy to promote fracture healing and prevent malunions. Prostaglandin E 2 (PGE 2 ) has been shown to have multiple biological effects in many tissues, including bone. PGE 2 causes significant increases in bone mass and bone strength when administered systemically or locally to the skeleton (9-11). However, due to side effects, including diarrhea, lethargy, and flushing, PGE 2 is an unacceptable therapeutic option for bone healing. PGE 2 binds to and elicits its pharmacological activity from four different cell surface receptor subtypes, EP1, -2, -3, and -4 (12-16). T...

Nine static models (seven basic and two mechanistic) and their respective cutoff values used for predicting cytochrome P450 3A (CYP3A) inhibition, as recommended by the US Food and Drug Administration and the European Medicines Agency, were evaluated using data from 119 clinical studies with orally administered midazolam as a substrate. Positive predictive error (PPE) and negative predictive error (NPE) rates were used to assess model performance, based on a cutoff of 1.25-fold change in midazolam area under the curve (AUC) by inhibitor. For reversible inhibition, basic models using total or unbound systemic inhibitor concentration [I] had high NPE rates (46-47%), whereas those using intestinal luminal ([I]gut) values had no NPE but a higher PPE. All basic models for time-dependent inhibition had no NPE and reasonable PPE rates (15-18%). Mechanistic static models that incorporate all interaction mechanisms and organ specific [I] values (enterocyte and hepatic inlet) provided a higher predictive precision, a slightly increased NPE, and a reasonable PPE. Various cutoffs for predicting the likelihood of CYP3A inhibition were evaluated for mechanistic models, and a cutoff of 1.25-fold change in midazolam AUC appears appropriate.

The results from this evaluation demonstrate the utility of PBPK methodology for the prediction of human pharmacokinetics. This methodology can be applied at different stages to enhance the understanding of the compounds in a particular chemical series, guide experiments, aid candidate selection and inform clinical trial design.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs).• Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme Ki, inhibitor and substrate concentrations and absorption rate for substrate and inhibitor. WHAT THIS STUDY ADDS• Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, fm) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation.• The current study supported the use of SIMCYP®, a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions. AIMSThe aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODSThe study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (fm); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP ® . RESULTSWhen in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONSThe current study demonstrated that the use of rhCYPs with SIMCYP ® provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

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