Prediction of Disease Progression and Clinical Response in Systemic Sclerosis: Experience From a Proof-of-Concept Trial.

OBJECTIVE: Using the modified Rodnan skin score (mRSS) as a surrogate for disease activity, a phase 2a study in patients with systemic sclerosis (SSc) measured efficacy of the autotaxin inhibitor ziritaxestat. Mathematical modeling of mRSS was used to predict disease progression, examine candidate trial designs, and predict the probability of successfully discriminating treatment effect. METHODS: Patients with SSc receiving 600 mg of ziritaxestat or placebo for 24 weeks were included, in addition to data up to week 52 of the open-label extension (OLE). Longitudinal mRSS data were described using a disease progression model; drug effect was a binary variable. Parameters used to predict the OLE mRSS outcome were estimated using data from the 24-week double-blind phase and validated with observed data. Three trial designs were simulated to identify which had the highest probability of detecting a treatment effect. Power to detect a treatment effect was quantified using the simulations. RESULTS: Maximum decreases from baseline in mRSS were 50.4% (ziritaxestat) and 34.7% (placebo). Study designs based on 300 patients randomized 2:1 or 1:1 to 600 mg of ziritaxestat or placebo had similar probabilities of detecting a significant treatment effect. Power to detect a treatment effect was >80% for all simulations. CONCLUSION: Disease progression and drug effect could be predicted beyond the range of observed data. This modeling and simulation approach may inform future trial design, including study duration, and predict the probability of success.

Monoamine transporter occupancy of a novel triple reuptake inhibitor in baboons and humans using positron emission tomography.

The selection of a therapeutically meaningful dose of a novel pharmaceutical is a crucial step in drug development. Positron emission tomography (PET) allows the in vivo estimation of the relationship between the plasma concentration of a drug and its target occupancy, optimizing dose selection and reducing the time and cost of early development. Triple reuptake inhibitors (TRIs), also referred to as serotonin-norepinephrine-dopamine reuptake inhibitors, enhance monoaminergic neurotransmission by blocking the action of the monoamine transporters, raising extracellular concentrations of those neurotransmitters. GSK1360707 [(1R,6S)-1-(3,4-dichlorophenyl)-6-(methoxymethyl)-4-azabicyclo[4.1.0]heptane] is a novel TRI that until recently was under development for the treatment of major depressive disorder; its development was put on hold for strategic reasons. We present the results of an in vivo assessment of the relationship between plasma exposure and transporter blockade (occupancy). Studies were performed in baboons (Papio anubis) to determine the relationship between plasma concentration and occupancy of brain serotonin reuptake transporter (SERT), dopamine reuptake transporter (DAT), and norepinephrine uptake transporter (NET) using the radioligands [(11)C]DASB [(N,N-dimethyl-2-(2-amino-4-cyanophenylthio) benzylamine], [(11)C]PE2I [N-(3-iodoprop-2E-enyl)-2ß-carbomethoxy-3ß-(4-methylphenyl)nortropane], and [(11)C]2-[(2-methoxyphenoxy)phenylmethyl]morpholine (also known as [(11)C]MRB) and in humans using [(11)C]DASB and [(11)C]PE2I. In P. anubis, plasma concentrations resulting in half-maximal occupancy at SERT, DAT, and NET were 15.16, 15.56, and 0.97 ng/ml, respectively. In humans, the corresponding values for SERT and DAT were 6.80 and 18.00 ng/ml. GSK1360707 dose-dependently blocked the signal of SERT-, DAT-, and NET-selective PET ligands, confirming its penetration across the blood-brain barrier and blockade of all three monoamine transporters in vivo.

Bayesian population approaches to the analysis of dose escalation studies.

In dose escalation studies cohorts of subjects are given increasing doses of a candidate drug to assess safety and tolerability, pharmacokinetics and pharmacological response. The escalation is carried on until a predefined stopping limit is achieved, often identified by a pharmacokinetic endpoint such as peak plasma concentration or area under the plasma concentration-time profile. In the present work, the application of Bayesian methodologies to Phase I dose escalation studies is explored. A Bayesian population model is devised, which provides predictions of dose-response and dose-risk curves, both for individuals already enrolled in the trial and for a new, previously untested subject. Empirical and fully Bayesian estimation algorithms are worked out. Such methods provide equivalent performances on both experimental and simulated datasets. With respect to previous work, it is quantitatively proven not only that a more general and flexible model is identifiable, but also that such flexibility is needed in real scenarios.

Discovery of N-[(2S)-5-(6-fluoro-3-pyridinyl)-2,3-dihydro-1H-inden-2-yl]-2-propanesulfonamide, a novel clinical AMPA receptor positive modulator.

A series of AMPA receptor positive allosteric modulators has been optimized from poorly penetrant leads to identify molecules with excellent preclinical pharmacokinetics and CNS penetration. These discoveries led to 17i, a potent, efficacious CNS penetrant molecule with an excellent pharmacokinetic profile across preclinical species, which is well tolerated and is also orally bioavailable in humans.

Nonparametric identification of population models: an MCMC approach.

The paper deals with the nonparametric identification of population models, that is models that explain jointly the behavior of different subjects drawn from a population, e.g., responses of different patients to a drug. The average response of the population and the individual responses are modeled as continuous-time Gaussian processes with unknown hyperparameters. Within a Bayesian paradigm, the posterior expectation and variance of both the average and individual curves are computed by means of a Markov Chain Monte Carlo scheme. The model and the estimation procedure are tested on both simulated and experimental pharmacokinetic data.