Design and qualify a PBPK model for existing oral anti-TB agents. Predict pharmacokinetics of long-acting nanoformulations of anti-TB agents in adults

Flexner, C

Owen, A

Podany, A

Rajoli, RKR

Siccardi, M

Swindells, S

University of Liverpool Repository

Simulation of long-acting administration of antituberculosisagents using pharmacokinetic modellingRajith KR Rajoli1, Anthony Podany2, Sue Swindells3, Charles Flexner4, Andrew Owen1, Marco Siccardi1Abst# O_131 Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, 2 College of Pharmacy, University of Nebraska Medical Center, Omaha, NE3 College of Medicine, University of Nebraska Medical Center, Omaha, NE 4 Johns Hopkins University, Baltimore, MarylandBackground• Current anti-TB administration strategies are based on long-term oral dosing • Oral administration is characterised by suboptimal adherence which represents a leading cause of treatment failure• 20 to 50% of patients fail to complete existing tuberculosis treatment • Injectable long-acting nano-formulations have been applied in numerous disease areas to simplify drug administration• Long-acting administration of anti-TB agents could represent a valuable pharmacological strategyCuneo WD, Snider DE Jr. Clin Chest Med. 1989 Sep;10(3):375-80.Munro SA et al. PLoS Med. 2007; 4(7): e238.Aims• Design and validate a physiologically based pharmacokinetic (PBPK) model for existing oral anti-TB agents• Simulate the pharmacokinetics of long-acting formulations of anti-TB agents in adult individualsPBPK model• Physiologically based pharmacokinetic (PBPK) modelling was used to inform the pharmacokinetics of anti-TB agents in adults Mathematical description of absorption, distribution, metabolism and elimination processes defining pharmacokinetics  PBPK modelling integrates in vitro and clinical data to simulate drug distribution in virtual population HeightWeightBMIGenderEthnicityAgeOrgan volumeCardiac outputRegional blood flows% of body fat Liver sizeRenal functionsCytochrome P450 expressionPlasma proteinTransporter expressionParameter correlation15*(Weight^0.75)(22 ± 3.5) kg/m2e(-0.6786+1.98*log(Height))((((1.20*BMI)+(0.23*Age)-16.2)*Weight)/100)Population variabilityVirtual populationVariabilityEssential PBPK ParametersPortal VeinBrainHeart TissueRV LVLungsSmall Intestine TissueEliminationPancreasSpleenKidneysThymusGonadsBonesMuscleAdiposeArteriesSkinMetabolismIM DepotCapillariesIntestinal MetabolismLiverStomach TissueStomachVeinsVolume of distributionIntramuscular release rateMetabolic clearancePharmacokineticsStudy designValidationPrediction• Existing available oral anti-TB formulations of bedaquiline, delamanidand rifapentine were validated in adults• Mean simulated values from 100 virtual individuals (aged 18-60 years) were compared with available clinical data• Virtual IM depot was included in the model to simulate IM administration  • Maximum feasible human IM dose of 2000 mg was assumed for pharmacokinetic predictions in the current study• Release rate was selected in order to obtain maximal exposure over the dosing intervalValidation against clinical formulationsClinical SimulatedDrug Cmax (µg/ml) Cmin (µg/ml) AUC (µg.h/ml) Cmax (µg/ml) Cmin (µg/ml) AUC (µg.h/ml)Bedaquiline(450 mg OD, single dose)13.76 ± 1.17 - ‡64.5 ± 26.9 3.59 ± 0.79 - ‡63.1 ± 15.6Delamanid(300 mg OD, day 10)20.41 ± 0.05 0.14 ± 0.04 †5.84 ± 0.99 0.45 ± 0.13 0.13 ± 0.08 †6.57 ± 2.31Rifapentine(10 mg/kg OD, day 14)321.7 (21.3-22.2) - †330 (284-340) 18.9 ± 2.4 8.6 ± 1.5 †327 ± 44†AUC0-24, ‡AUC0-1441van Heeswijk RP, Dannemann B, Hoetelmans RM., J Antimicrob Chemother. 2014 Sep;69(9):2310-8.2Deltyba, Assessment report, EMA, 2014.3Dooley KE et al. Clin Pharmacol Ther. 2012 May ; 91(5).Release rate optimisationRelease rate – 0.04 h-1Release rate – 0.004 h-1Release rate – 0.0004 h-1Therapeutic cut-offrepresentativeDrugAUC(Mean ± SD)(µg.h/ml)Cmax(Mean ± SD)(µg/ml)Ctrough(Mean ± SD)(µg/ml)Cut-off limit(µg/ml)Bedaquiline 271 ± 65 0.72 ± 0.16 0.14 ± 0.04 1.6 (ECOFF)Delamanid 89 ± 16 0.23 ± 0.04 0.05 ± 0.01 0.04 (ECOFF)Rifapentine 1639 ± 160 4.12 ± 0.38 0.88 ± 0.09 0.06 (MIC)Prediction - SummaryIM Dose – 2000 mg/30 days IM release rate – 0.0025 h-1Limitations• Activity of transporters can affect distribution and elimination patterns• Drugs with high lipophilicity tend to diffuse through the lymphatic circulation rather than through blood• The technological complexities associated with reformulation may constitute a barrier for some anti-TB agents• Long term stability of anti-TB agents in potential long-acting formulations is unknown Conclusion• This theoretical approach could assist in informing the design of long-acting formulation for IM administration of anti-TB agents• PBPK modelling represents a predictive tool to rationalise anti-TB agent pharmacokinetics and hypothesise potential applications of long-acting anti-TB therapy• Lack of clear pharmacodynamics cut-offs and clinical validation of alternative combinations complicates the selection of suitable long-acting candidates• Long-acting formulations could also find potential application in the treatment of latent TB or chemoprophylaxisAcknowledgements• Dr. Marco Siccardi • Prof. Andrew Owen• Prof. Charles Flexner• Asst. Prof. Anthony Podany• Prof. Sue Swindells• Prof. David Back• Prof. Saye Khoo• Prof. Steve Rannard• Dr. Paul Curley• Dr. James Hobson• Dr. Adeniyi Olagunju• Dr. Lee Tatham• Dr. José Moltó• Dr. Catia Marzolini• Dr. Neill Liptrott• Dr. Adny Henrique Silva• Christopher David• Dr. Darren Michael Moss• Dr. Owain Roberts• Dr. Sharon Murphy• Christina Chan• Louise Tidbury• Justin Chiong• Rohan Gurjar• Ana Jiminez-Valverde• Megan Neary• Rana Abutaima• Gini Joshua• Hannah Kinvig• Colleagues in the department of Molecular and Clinical PharmacologyLong-Acting/Extended Release Antiretroviral Resource Program 

Simulation of long-acting administration of antituberculosis agents using pharmacokinetic modelling

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