Liviawati S. Wu (1), Nele Goeyvaerts (2), Alberto Russu (2), Ilham Smyej (2), Sophie Lachau-Durand (2), An De Creus (2), Florence Herschke (2), Marianne Tuefferd (2), Tse-I Lin (1), Joris Vandenbossche (2)
(1) Alios BioPharma, Inc., part of the Janssen Pharmaceutical Companies, South San Francisco, CA, USA; (2) Janssen Research and Development, Beerse, Belgium
Objectives: TLR7 agonists stimulate the innate immunity by inducing cytokines and interferon-stimulated genes (ISGs) to trigger an antiviral or antitumor effect. The aim was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for an oral, small molecule TLR7 agonist that characterizes the plasma exposures and type I interferon (IFN)-dependent innate immune response in healthy cynomolgus monkeys. To this purpose, two PD biomarkers were considered: IFN-γ-inducible protein 10 (IP‑10) and IFN-stimulated gene 15 (ISG15). A secondary objective is to use mathematical models to interrogate the presence and interplay of amplification, receptor downregulation and circadian rhythm[1-5], physiological processes which are known to be involved in this pathway.
Methods: Plasma PK data were pooled from 7 monkey studies, involving 1714 plasma concentrations from 126 monkeys with doses up to 15 mg/kg and at different regimens. IP-10 plasma concentrations (n=1673) were available from 110 monkeys and ISG15 microarray data (n=773) were available from 50 monkeys. Dose was implemented in the model as per-kg basis with monkey weights ranging from 2.2 to 6 kg. The data were analyzed by a non-linear mixed effects modeling approach implemented in NONMEM V7.3.0[6]. Observations below the lower limit of quantification were accounted for by using the M3 method[7]. IP-10 and ISG15 were modeled sequentially based on individual PK estimates. Indirect response models with linear versus Emax stimulation on the production rate constant (kin) were tested. Model selection was guided by the objective function value, diagnostic plots, standard error of parameters, evaluation of condition number, shrinkage, and visual predictive checks.
Results: Exposures increased in a greater than dose proportional manner. A three-compartment model with saturable pre-systemic target-mediated drug disposition (TMDD)[8]-like elimination, transit absorption, first-order distribution to 2 peripheral compartments, and first-order elimination from central compartment, was shown to adequately describe the pharmacokinetics of the TLR7 agonist in cynomolgus monkeys. The TMDD mechanism is novel for small molecule TLR7 agonists, pointing to possible pre-systemic TLR7 engagement in the gut-associated lymphoid tissues and liver, which was also suggested as the mechanism of GS-9620, another compound of this class[9]. Based on the mean population estimates, the fraction of drug absorbed increased from 2.6% at 0.05 mg/kg, 3.1% at 0.5 mg/kg, 4.0% at 1 mg/kg, 19.7% at 6 mg/kg, to 38.9% at 15 mg/kg. There was no evidence of time-dependent PK with multiple dosing. Females displayed faster absorption and higher bioavailability. The indirect response model used to describe the ISGs (IP-10 and ISG15) incorporated signal transduction using transit compartments. The driver for the stimulation of kin of both IP-10 and ISG15 were slope functions of plasma concentration with an exponent estimated. An Emax model did not result in a statistically significant better fit. Therefore, the model with slope function was deemed as the most parsimonious given the available data. Inclusion of circadian rhythm and feedback did not significantly improve model fit. Further experiments and data are needed to demonstrate these processes conclusively.
Conclusions: A novel semi-mechanistic population PK/PD model has been developed for a small molecule TLR7 agonist in cynomolgus monkeys that could prove useful for other TLR7 agonists as well. Inclusion of saturable pre-systemic TMDD adequately described the nonlinear pharmacokinetics. Stimulation of the innate immune response, as measured by the induction of ISGs (IP-10 and ISG15), was adequately described using an indirect response model with a slope function. The model will be used to translate PK/PD from monkey to human to inform dose setting for antiviral hepatitis treatment.
References:
[1] Ivashkiv LB, Donlin LT. Regulation of type I interferon responses. Nat Rev Immunol (2014) 14(1): 36-49.
[2] Ma F, Li B, Yu Y, Iyer SS, Sun M, Cheng G. Positive feedback regulation of type I interferon by the interferon-stimulated gene STING. EMBO Rep (2015) 16(2): 202-12.
[3] Honda K, Takaoka A, Taniguchi T. Type I interferon gene induction by the interferon regulatory factor family of transcription factors. Immunity (2006) 25(3): 349-60.
[4] Shiba M, Nonomura N, Nakai Y, Nakayama M, Takayama H, Inoue H, Tsujimura A, Nishimura K, Okuyama A. Type I interferon receptor expression: its circadian rhythm and downregulation after interferon-alpha administration in peripheral blood cells from renal cancer patients. Int J Urol (2009) 16(4): 356-9.
[5] Fidock MD, Souberbielle BE, Laxton C, Rawal J, Delpuech-Adams O, Corey TP, Colman P, Kumar V, Cheng JB, Wright K, Srinivasan S, Rana K, Craig C, Horscroft N, Perros M, Westby M, Webster R, van der Ryst E. The innate immune response, clinical outcomes, and ex vivo HCV antiviral efficacy of a TLR7 agonist (PF-4878691). Clin Pharmacol Ther (2011) 89(6): 821-9.
[6] Beal SL, Sheiner LB, Boeckmann AJ, Bauer RJ (Eds.) NONMEM User Guides. 1989-2015. Icon Development Solutions, Ellicott City, Maryland, USA.
[7] Ahn JE, Karlsson MO, Dunne A, Ludden TM. Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn (2008) 35(4): 401-21.
[8] Mager DE. Target-mediated drug disposition and dynamics. Biochem Pharmacol (2006) 72(1):1-10.
[9] Fosdick A, Zheng J, Pflanz S, Frey CR, Hesselgesser J, Halcomb RL, Wolfgang G, Tumas DB. Pharmacokinetic and pharmacodynamic properties of GS-9620, a novel toll-like receptor 7 agonist, demonstrate interferon-stimulated gene induction without detectable serum interferon at low oral doses. J Pharmacol Exp Ther (2014) 348(1): 96-105.
Reference: PAGE 27 (2018) Abstr 8694 [www.page-meeting.org/?abstract=8694]
Poster: Drug/Disease Modelling - Infection