Wille, D. R. | Search bioRxiv

bioRxiv2026

Nonclinical pharmacokinetics and relative efficacy of the first 25 novel tuberculosis drug combinations from the PAN-TB consortium: Use of the BALB/c relapsing mouse model and combination pharmacokinetics within a modeling-based framework

The Project to Accelerate New Treatments for Tuberculosis (PAN-TB) aims to accelerate development of shorter, simpler and safer pan-TB combinations, effective for use in both Drug Susceptible (DS)- and Drug Resistant (DR)- TB patients. Towards this aim, bactericidal and sterilizing activity of 25 priority 4-drug combinations was evaluated at doses targeting clinically relevant exposures, in the BALB/c relapsing mouse model of TB. The combinations comprised 8 PAN-TB drugs and candidates: bedaquiline (B), pretomanid (Pa), delamanid (Del), quabodepistat (Q), sutezolid (Sut), GSK2556286 (286), GSK3211830 (830) and ganfeborole (GSK3036656, (656)). Combination PK studies in infected mice enabled dose selection and a population-PK approach guided dosing so that compounds should achieve mean AUC0-24 within 2-fold of their clinical target exposures during the efficacy studies. All test combinations showed time-dependent bactericidal activity, with six regimens reducing lung bacterial burdens below the limit of detection with 8 weeks treatment, similar to the comparator BPaMZ (M is moxifloxacin and Z as pyrazinamide). Cure/Relapse data were modelled to derive population time to cure 90% mice (T90) values. Fifteen PAN-TB combinations had T90s of less than 5 months, sterilizing mice faster than the standard of care for drug susceptible TB, RHZE/RH. The best-performing PAN-TB combinations, BPa830Sut, BPa286Sut and BQSut286, cured 90% of mice in less than 3 months. These 3 top-ranked 4-drug combinations are all centered on a diarylquinoline (B)/oxazolidinone (Sut) core, together with the nitroimidazole (Pa) or a DprE1 inhibitor (Q) plus a novel agent such as the LeuRS inhibitor (830) or the Rv1625c agonist (286).

microbiology↗

bioRxiv2018

Benchmarking network propagation methods for disease gene identification

BackgroundIn-silico identification of potential disease genes has become an essential aspect of drug target discovery. Recent studies suggest that one powerful way to identify successful targets is through the use of genetic and genomic information. Given a known disease gene, leveraging intermolecular connections via networks and pathways seems a natural way to identify other genes and proteins that are involved in similar biological processes, and that can therefore be analysed as additional targets.\n\nResultsHere, we systematically tested the ability of 12 varied network-based algorithms to identify target genes and cross-validated these using gene-disease data from Open Targets on 22 common diseases. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. We also compared several cross-validation schemes and showed that different choices had a remarkable impact on the performance estimates. When seeding biological networks with known drug targets, we found that machine learning and diffusion-based methods are able to find novel targets, showing around 2-4 true hits in the top 20 suggestions. Seeding the networks with genes associated to disease by genetics resulted in poorer performance, below 1 true hit on average. We also observed that the use of a larger network, although noisier, improved overall performance.\n\nConclusionsWe conclude that machine learning and diffusion-based prioritisers are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large effect of several factors on prediction performance, especially the validation strategy, input biological network, and definition of seed disease genes.

genetics↗