Back, D

Owen, A

Rajoli, RKR

Rannard, S

Siccardi, M

University of Liverpool Repository

Rajith Kumar Reddy Rajoli1, David Back1, Steve Rannard2, Andrew Owen1, Marco Siccardi1.1 - Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK 2- Department of Chemistry, University of Liverpool, UKMethods• Virtual patients were generated using a population physiology model [4].Age, BMI and weight were used to allometrically scale organ weights andcardiac output [4]. A virtual population of healthy Caucasian individualswith a mean age of 39 years, (range 18-60) was generated.• Validated equations were used for the calculation of volume of distributionand processes regulating absorption, distribution and elimination [5-9].• Physicochemical properties, in vitro apparent permeability, in vitrointrinsic clearance and cytochrome P450 induction were obtained fromthe literature.• PK of ARVs was predicted using Simbiology (in MATLAB, version 2013b).• Simulations for oral PK were first developed to validate the PBPK modelsagainst available clinical data. The PBPK model for rilpivirine (RPV) wasthen validated against the published PK of long-acting RPV to legitimisethe approach. Finally, the PK for IM administration of all the ARVs atvarious dose and release rate combinations was simulated.• For the first validation, oral absorption was simulated using acompartmental absorption and transit model [6]. For IM depotsimulations, a discrete compartment was introduced to represent muscletissue containing the depot, and release of drug from the depot into theblood plasma was assumed to follow dose-dependent first-order kinetics.• Dose and release rate combinations of the 8 ARVs were optimised to givepredicted median plasma concentrations above the protein bindingcorrected IC95 (PBIC95) or IC95 values 7- or 30-days after administration.Results• The simulated PK parameters for oral administration were inagreement with previously published clinical data (data notshown).• Validation of the PBPK model was subsequently conductedagainst an existing long-acting IM formulation of RPV (600mg; 100 mg mL-1) [10]. The mean values for AUC were 84.0ng mL-1 h vs. 83.38 ± 33.34 ng mL-1 h, Cmax 96.7 ng mL-1 vs.86.73 ± 30.51 ng mL-1 and Ctrough 15.7 vs. 11.81 ± 6.3 ng mL-1for clinical versus predicted data with a predicted releaserate of 0.0011 ± 0.0001 h-1 for the clinical IM formulation(Figure 2).• A summary of the predicted values for AUC, Cmax and Cmin for8 ARVs along with dose and release rate combinationspredicted to be optimal is shown in Table 1.•Dolutegravir, efavirenz, emtricitabine, raltegravir, tenofovirand RPV were predicted to be the suitable candidates formonthly IM injection as shown in Figure 3.Optimisation of Intramuscular Sustained Release-Nano-Formulations Using In Silico ModellingFigure 3. Simulated PK of IM sustained release NFs using PBPK modelling (a) Dolutegravir (b) Efavirenz (c) Emtricitabine (d) Raltegravir (e) RPV (f) Tenofovir. Mean Mean ± S.D. PBIC95/IC95References1. Naswa S. and Marfatia Y. S. Indian J Sex Transm Dis. 2011 Jan-Jun; 32(1): 1–8.2. Heneine W. and Kashuba A. Cold Spring Harb Perspect Med. 2012 March; 2(3): a007419.3. Baert, L., et al., Eur J Pharm Biopharm. 2009. 72(3): p. 502-508.4. Bosgra, S., et al., Crit Rev Toxicol. 2012. 42(9): p. 751-767.5. Peters, S., Clin Pharmacokinet. 2008. 47(4): p. 261-275.6. Yu, L.X. and G.L. Amidon, Int J Pharm. 1999. 186(2): p. 119-125.7. Gertz, M., et al., Drug Metab Dispos. 2010. 38(7): p. 1147-1158.8. Sun, D., et al., Pharm Res. 2002. 19(10): p. 1400-1416.9. Poulin, P. and F.P. Theil, J Pharm Sci, 2002. 91(1): p. 129-56.10. van ′t Klooster, G., et al., Antimicrob Agents Chemother. 2010. 54(5): p. 2042-2050.11. Wang LH, et al., Poster (#4546) presented at XIV International AIDS Conference 2002.12. Palleja S et al., presented at 49th ICAAC. 2009.13. TDM, SOP Reporting Algorithms.14. Azijn, H., et al., Antimicrob Agents Chemother. 2010. 54(2): p. 718-727.15. Francois Raffi, M.A.W., Retrovirology, 2012. 9: p. 110.16. Min, S., et al., Antimicrob Agents Chemother. 2010. 54(1): p. 254-258.17. Acosta, E.P., et al., Antimicrob Agents Chemother. 2012. 56(11): p. 5938-5945.18. Jones HM and Rowland-Yeo K. CPT Pharmacometrics Syst. Pharmacol. (2013) 2, e63.19. Castellino S., et al., Antimicrob Agents Chemother. 2013. 57(8): p. 3536-3546.Rajith Kumar Reddy Rajolirrajoli@liverpool.ac.uk T +44 151 794 5565F +44 151 794 5656Poster 518Conclusion• These data are theoretical and currently there is no evidence to confirmor refute that these dose / release rate combinations can be achieved byavailable technologies.• Candidate ARVs with potential for reformulation into IM depot wereidentified, providing the technological complexities associated withreformulation can be overcome for these agents.• Based on known clearance of RPV, monthly exposure from 250 mg (2.5mL equivalent for latest existing formulation) is theoretically achievableif release rate can be tuned to 0.002 h-1.• PBPK modelling may be a useful tool for defining productcharacteristics for sustained-release NF development.Overview• Antiretrovirals (ARVs) can find clinical application not only in the treatment of HIVinfection but also in its prevention. Recently pre-exposure prophylaxis (PrEP)strategies have been developed for subjects who are at high risk of acquiring theinfection [1].• Currently available oral formulations necessitate lifelong, daily dosing and asuboptimal adherence, places the patient at risk of treatment failure and low ratesof protection for PrEP [2].• The introduction of injectable sustained-release nano-formulations (NFs) couldrepresent a pharmacological option opening the possibility to simplify dosingregimens, increase adherence, reduce the amount of drug consumed and thusdecrease the overall cost of the treatment and PrEP [3].• Physiologically based pharmacokinetic (PBPK) modelling is the mathematicaldescription of anatomical, physiological and molecular processes defining drugdistribution (Figure 1b), through the integration of drug characteristics andpatient-specific factors (Figure 1a) [18].• The aim of this study was to simulate the PK of intramuscular (IM) sustainedrelease NFs using PBPK modelling. Existing ARVs available as oral formulationswere assessed for compatibility. Theoretical target dose and release ratecombinations for once weekly and once monthly formats were identified.(a) (b)Figure 1. (a) Co-relation between some of the parameters involved in PBPK modelling.(b) A diagrammatic representation of the anatomy of a virtual individual in the PBPK model.a) b)c) d) e) f)Figure 2. Validation of the PBPK strategy against clinical data for an existing RPV sustained-release formulation (600 mg; 100 mg mL-1).Simulated MeanSimulated S.D.Clinical Formulation

Optimisation of Intramuscular Sustained Release-Nano-Formulations Using In Silico Modelling

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