Quantitative Prediction and Clinical Observation of a CYP3A Inhibitor-Based Drug-Drug Interactions with MLN3897, a Potent C-C Chemokine Receptor-1 Antagonist

Abstract

A novel in vitro model was recently developed in our laboratories for the prediction of magnitude of clinical pharmacokinetic drugdrug interactions (DDIs), based on reversible hepatic cytochrome P450 (P450) inhibition. This approach, using inhibition data from human hepatocytes incubated in human plasma, and quantitative P450 phenotyping data from hepatic microsomal incubations, successfully predicted DDIs for 15 marketed drugs with ketoconazole, a strong competitive inhibitor of CYP3A4/5, generally used to demonstrate a "worst-case scenario" for CYP3A inhibition. In addition, this approach was successfully extended to DDI predictions with the moderate competitive CYP3A inhibitor fluconazole for nine marketed drugs. In the current report, the general applicability of the model has been demonstrated by prospectively predicting the degree of inhibition and then conducting DDI studies in the clinic for an investigational CCR1 antagonist MLN3897, which is cleared predominantly by CYP3A. The clinical studies involved treatment of healthy volunteers (n ϭ 17-20), in a crossover design, with ketoconazole (200 mg b.i.d.) or fluconazole (400 mg once a day), while receiving MLN3897. Administration of MLN3897 and ketoconazole led to an average 8.28-fold increase in area under the curve of plasma concentration-time plot (AUC) of MLN3897 at steady state, compared with the 8.33-fold increase predicted from the in vitro data. Similarly for fluconazole, an average increase of 3.93-fold in AUC was observed for MLN3897 in comparison with a predicted value of 3.26-fold. Thus, our model reliably predicted the exposure changes for MLN3897 in interaction studies with competitive CYP3A inhibitors in humans, further strengthening the utility of our in vitro model.Prediction of clinical DDIs using in vitro studies is one of the major challenges in the pharmaceutical industry. The main utility of such DDI predictions is to help foresee any safety issues anticipated from higher exposures and thus help design clinical trials with better safety. In some instances, clinical DDI studies can be avoided if no significant pharmacokinetic interaction is predicted. Traditionally, DDI predictions have been based on the ratio of the inhibitor concentration [I] and the enzyme inhibition constant K i ([I]/K i ratio) (Rostami-Hodjegan and Tucker, 2004;Ito et al., 2004;Obach et al., 2006; 2006 FDA draft guidance on DDI studies at http://WWW.FDA.gov/cder/ guidence/6695dft.html). Although this approach had some successes in predicting DDIs, reliability of predictions still remains a challenge (Blanchard et al., 2004;Cook et al., 2004;Ito et al., 2004;Bachmann 2006;Obach et al., 2006). Difficulty remains both in estimating the physiological concentration of the inhibitor at the enzyme site and in reliably determining K i , which often is affected by the experimental conditions used. Factors such as the protein concentration in the incubation, the substrate of choice, and the enzyme source (Thummel and Wilkinson 1998;Galetin et al., 2006;Lu et al.,...

Alcohol consumption effect on antiretroviral therapy and HIV-1 pathogenesis: role of cytochrome P450 isozymes

“…Use of a CYP3A4 inhibitor can prolong the elimination of drug substrates leading to increased half-life and toxic drug accumulation in the liver [80]. Alternatively, a CYP3A4 inducer may lead to suboptimal drug concentrations and a reduced therapeutic effect of HAART drugs due to faster metabolism [81].…”

N-aryl pyrazoles, indazoles and azaindazoles as antagonists of CC chemokine receptor 1: patent cooperation treaty applications WO2010/036632, WO2009/134666 and WO2009/137338

“…This was N-arylated with 4-fluoroiodobenzene using copper iodide as the catalyst. The product was saponified to provide the carboxylic acid 18, which was subsequently coupled with amine 20 (itself synthesized in three steps from 19) using PyBOP to provide amide 21. The bromine of 21 was exchanged for a nitrile using zinc cyanide and palladium catalysis to provide the desired 6-cyano-1-(4-fluorophenyl)-1H-indazole-4-carboxylic acid-4-methylsulfamoyl-benzylamide 8. The synthesis of the azaindazole 9 (CCR1 IC 50 = 0.2 nM), a preferred compound from WO2010/036632, began with 3,5-dibromo-4-pyridinecarboxaldehyde 22 (Figure 5), which was condensed with para-fluorophenyl hydrazine and then cyclized with catalytic copper (I) iodide at 120 C to give 4-bromo-1-(4-fluorophenyl)-1H-pyrazolo[3,4-c]pyridine 23. This was carbonylated using palladium catalysis, and the resultant ester was saponified to provide carboxylic acid 24. The enantioenriched amine coupling partner was prepared using Ellman's sulfinamide chemistry [27].…”

“…Interestingly, compound 3 did show activity in an epicutaneous nickel challenge model conducted with human volunteers [18]. A compound from Millenium, MLN-3897 (4) [19], also failed to meet its clinical end points in a Phase II trial in rheumatoid arthritis [20]. Chemocentryx and GlaxoSmithKline, apparently undeterred by the data with 3 and 4, are developing CCX-354 for the treatment of rheumatoid arthritis; as of this writing, the Phase I--II trial is currently recruiting.…”

Human Bile Collection for ADME Studies

“…Thus, relying on fecal data alone could lead to an underestimation of the extent of metabolism of the compound or the importance of a particular metabolic pathway. Both the extent of human metabolism (i.e., the overall fraction of the drug metabolized in vivo ) and the fraction metabolized via a particular pathway are required in order to quantitatively understand the extent of different enzymes involved in the clearance of the compound (Lu et al, 2007(Lu et al, , 2009(Lu et al, , 2010, which in turn helps to more accurately predict the DDI potential for those enzymes.…”