A comparison of antimicrobial regimen outcomes and antibiogram development in microbial keratitis: a prospective cohort study in Alexandria, Egypt.

INTRODUCTION: Antimicrobial resistance in microbial keratitis has not been previously explored in Alexandria. We aim to recommend effective therapies through identification of etiological agents, determination of antimicrobial susceptibilities, and comparing outcomes of empiric topical antimicrobials. METHODS: In this 2022 prospective cohort conducted in Alexandria Main University Hospital cornea clinic, antimicrobial susceptibilities of isolated microorganisms from corneal scrapings were detected and antibiograms were developed. Bacterial (BK), fungal (FK), or mixed fungal/bacterial keratitis (MFBK) patients on empiric regimens were compared for ulcer healing, time-to-epithelialization, best-corrected visual acuity, interventions, and complications. RESULTS: The prevalent microorganisms in 93 positive-cultures were coagulase-negative staphylococci (CoNS, 30.1%), Pseudomonas aeruginosa (14%), and Aspergillus spp. (12.9%). CoNS were susceptible to vancomycin (VAN, 100%) and moxifloxacin (MOX, 90.9%). Gram-negative bacteria showed more susceptibility to gatifloxacin (90.9%) than MOX (57.1%), and to gentamicin (GEN, 44.4%) than ceftazidime (CAZ, 11.8%). Methicillin-resistance reached 23.9% among Gram-positive bacteria. Fungi exhibited 10% resistance to voriconazole (VRC). Percentages of healed ulcers in 49 BK patients using GEN + VAN, CAZ + VAN and MOX were 85.7%, 44.4%, and 64.5%, respectively (p = 0.259). Their median time-to-epithelialization reached 21, 30, and 30 days, respectively (log-rank p = 0.020). In 51 FK patients, more ulcers (88.9%) healed with natamycin (NT) + VRC combination compared to VRC (39.1%) or NT (52.6%) (p = 0.036). Their median time-to-epithelialization was 65, 60, and 22 days, respectively (log-rank p < 0.001). The VRC group required more interventions (60.9%) than NT + VRC-treated group (11.1%) (p = 0.018). In 23 MFBK patients, none healed using NT + CAZ + VAN, while 50% healed using VRC + CAZ + VAN (p = 0.052). Regimens had comparable visual outcomes and complications. CONCLUSION: Based on the higher detected susceptibility, we recommend empiric MOX in suspected Gram-positive BK, gatifloxacin in Gram-negative BK, and GEN + VAN in severe BK. Due to better outcomes, we recommend NT + VRC in severe FK. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT05655689. Registered December 19, 2022- Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05655689?cond=NCT05655689.&draw=2&rank=1.

In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors.

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with Rval 2= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best Rval 2 for both validation sets. For predictions that have high bias, improvement of Rval 2 from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.