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Induction of IFNα in the upper airways via activation of TLR7 represents a novel immunomodulatory approach to the treatment of allergic asthma. Exploration of 8-oxoadenine derivatives bearing saturated oxygen or nitrogen heterocycles in the N-9 substituent has revealed a remarkable selective enhancement in IFNα inducing potency in the nitrogen series. Further potency enhancement was achieved with the novel (S)-pentyloxy substitution at C-2 leading to the selection of GSK2245035 (32) as an intranasal development candidate. In human cell cultures, compound 32 resulted in suppression of Th2 cytokine responses to allergens, while in vivo intranasal administration at very low doses led to local upregulation of TLR7-mediated cytokines (IP-10). Target engagement was confirmed in humans following single intranasal doses of 32 of ≥20 ng, and reproducible pharmacological response was demonstrated following repeat intranasal dosing at weekly intervals.

Optimization of a lead series of PI3Kδ inhibitors based on a dihydroisobenzofuran core led to the identification of potent, orally bioavailable compound 19. Selectivity profiling of compound 19 showed similar potency for class III PI3K, Vps34, and PI3Kδ, and compound 19 was not well-tolerated in a 7-day rat toxicity study. Structure-based design led to an improvement in selectivity for PI3Kδ over Vps34 and, a focus on oral phramacokinetics properties resulted in the discovery of compound 41, which showed improved toxicological outcomes at similar exposure levels to compound 19.

PurposeWe developed and tested a novel Quantitative Structure-Activity Relationship (QSAR) model to better understand the physicochemical drivers of pulmonary absorption, and to facilitate compound design through improved prediction of absorption. The model was tested using a large array of both existing and newly designed compounds.MethodsPulmonary absorption data was generated using the isolated perfused respiring rat lung (IPRLu) model for 82 drug discovery compounds and 17 marketed drugs. This dataset was used to build a novel QSAR model based on calculated physicochemical properties. A further 9 compounds were used to test the model’s predictive capability.ResultsThe QSAR model performed well on the 9 compounds in the “Test set” with a predicted versus observed correlation of R2 = 0.85, and >65% of compounds correctly categorised. Calculated descriptors associated with permeability and hydrophobicity positively correlated with pulmonary absorption, whereas those associated with charge, ionisation and size negatively correlated.ConclusionsThe novel QSAR model described here can replace routine generation of IPRLu model data for ranking and classifying compounds prior to synthesis. It will also provide scientists working in the field of inhaled drug discovery with a deeper understanding of the physicochemical drivers of pulmonary absorption based on a relevant respiratory compound dataset.

The School of Pharmacy and Pharmaceutical Sciences at Trinity College Dublin hosted the "1 Workshop on Drug Transporters in the Lungs" in September 2016 to discuss the impact of transporters on pulmonary drug disposition and their roles as drug targets in lung disease. The workshop brought together about 30 scientists from academia and pharmaceutical industry from Europe and Japan and addressed the primary questions: What do we know today, and what do we need to know tomorrow about transporters in the lung? The 3 themes of the workshop were: (1) techniques to study drug transporter expression and actions in the lungs; (2) drug transporter effects on pulmonary pharmacokinetics-case studies; and (3) transporters as drug targets in lung disease. Some of the conclusions of the workshop were: suitable experimental models that allow studies of transporter effects are available; data from these models convincingly show a contribution of both uptake and efflux transporters on pulmonary drug disposition; the effects of transporters on drug lung PK is now better conceptualized; some transporters are associated with lung diseases. However, more work is needed to establish which of the available models best translate to the clinical situation.