Aspergillus spp. infections remain a global concern, with ∼30% attributable mortality of invasive aspergillosis (IA). VT-1598 is a novel fungal CYP51 inhibitor designed for exquisite selectivity versus human CYP enzymes to achieve a maximal therapeutic index and therefore maximal antifungal efficacy. Previously, its broad-spectrum in vitro antifungal activity was reported. We report here the pharmacokinetics (PK) and pharmacodynamics (PD) of VT-1598 in neutropenic mouse models of IA. The plasma area-under-the-curve (AUC) of VT-1598 increased nearly linearly between 5 and 40 mg/kg after 5 days of QD administration (155 and 1033 μg*h/ml, respectively), with a further increase with 40 mg/kg BID dosing (1354 μg*h/ml). When A. fumigatus isolates with in vitro susceptibilities of 0.25 and 1.0 μg/ml were used in a disseminated IA model, VT-1598 treatment produced no decrease in kidney fungal burden at QD 10 mg/kg, intermediate decreases at QD 20 mg/kg and maximum or near maximum decreases at 40 mg/kg QD and BID. The PK/PD relationships of AUCfree/MIC for 1-log killing for the two strains were 5.1 and 1.6 h, respectively, similar to values reported for approved CYP51 inhibitors. In a survival study where animals were observed for 12 days after the last treatment, survival was 100% at the doses tested (20 and 40 mg/kg QD), and fungal burden remained suppressed even though drug wash-out was complete. Similar dose-dependent reductions in lung fungal burden were observed in a pulmonary model of IA. These data strongly support further exploration of VT-1598 for the treatment of this lethal mold infection.

Article Title: Kinetic modelling of the role of the aldehyde dehydrogenase enzyme and the breast cancer resistance protein in drug resistance and transport Year of publication: 2010Link to publication: http://www.cmpbjournal.com/home Link to published article: http://dx.doi. org/10.1016/j.cmpb.2010.06.008 Copyright statement: This is the author's version of a work that was accepted for publication in Computer Methods and Programs in Biomedicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods and Programs in Biomedicine, [Vol.104, (No. Verification of the proposed model is achieved using scanning-laser microscopy data from live human breast cancer cells. Before estimating the unknown model parameters from the experimental in vitro data it is essential to determine parameter uniqueness (or otherwise) from this imposed output structure. This is formally performed as a structural identifiability analysis, which demonstrates that all of the unknown model parameters are uniquely determined by the output structure corresponding to the experiment.

Article Title: Kinetic modelling of the role of the aldehyde dehydrogenase enzyme and the breast cancer resistance protein in drug resistance and transport Year of publication: 2010Link to publication: http://www.cmpbjournal.com/home Link to published article: http://dx.doi. org/10.1016/j.cmpb.2010.06.008 Copyright statement: This is the author's version of a work that was accepted for publication in Computer Methods and Programs in Biomedicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods and Programs in Biomedicine, [Vol.104, (No. Verification of the proposed model is achieved using scanning-laser microscopy data from live human breast cancer cells. Before estimating the unknown model parameters from the experimental in vitro data it is essential to determine parameter uniqueness (or otherwise) from this imposed output structure. This is formally performed as a structural identifiability analysis, which demonstrates that all of the unknown model parameters are uniquely determined by the output structure corresponding to the experiment.