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Staphylococcus aureus is a major cause of severe invasive infections. The increasing incidence of infections caused by antibiotic-resistant strains such as methicillin-resistant S. aureus (MRSA), calls for exploration of new approaches to treat these infections. Mupirocin is an antibiotic with a unique mode of action that is active against MRSA, but its clinical use is restricted to topical administration because of its limited plasma stability and rapid degradation to inactive metabolites. Mupirocin was identified by a machine learning approach to be suitable for nano-liposome encapsulation. The computational predictions were verified experimentally and PEGylated nano-liposomal formulation of mupirocin (Nano-mupirocin) was developed. The aim of this study was to investigate the efficacy of this formulation when administered parenterally for the treatment of S. aureus invasive infections. Nano-mupirocin exhibited prolonged half-life of active antibiotic and displayed superior antimicrobial activity against S. aureus than free mupirocin in the presence of plasma. Parenteral application of Nano-mupirocin in a murine model of S. aureus bloodstream infection resulted in improved antibiotic distribution to infected organs and in a superior therapeutic efficacy than the free drug. Parenterally administered Nano-mupirocin was also more active against MRSA than free mupirocin in a neutropenic murine lung infection model. In addition, Nano-mupirocin was very efficiently taken up by S. aureus-infected macrophages via phagocytosis leading to enhanced delivery of mupirocin in the intracellular niche and to a more efficient elimination of intracellular staphylococci. The outcome of this study highlights the potential of Nano-mupirocin for the treatment of invasive MRSA infections and support the further clinical development of this effective therapeutic approach.

Mupirocin is an antibiotic having a unique mode of action, not shared by any other therapeutically available antibiotic. However, due to its rapid elimination following injection and high protein binding, current therapeutic use is limited to topical administration. Computational methods have identified mupirocin as a good candidate for delivery via long-circulating nano-liposomes. Formulating mupirocin in such liposomes to form Nano-mupirocin protects the drug in the circulation, enabling therapeutic efficacy. This was demonstrated using two different animal models that served as a proof of concept: the mice necrotizing fasciitis and rabbit endocarditis models. In both animal models, mupirocin administered intravenously (IV) lacked therapeutic efficacy, while the Nano-mupirocin administered IV was efficacious. In both mice and rabbits the pharmacokinetic (PK) profile following IV injection of Nano-mupirocin showed significantly greater AUC and elimination half-life of Nano-mupirocin compared to the free drug. In addition, in mice we also demonstrated significant drug distribution into the disease site. These PK profiles may explain Nano-mupirocin’s superior therapeutic efficacy. To the best of our knowledge, this is the first study demonstrating that systemic activity of mupirocin is feasible. Therefore, Nano-mupirocin can be considered a novel and unique parenteral antibiotic candidate drug.

Neisseria gonorrhoeae is an urgent antibiotic-resistant threat. This study determined the MICs of mupirocin to be 0.0039 to 0.0625 μg/ml for 94 N. gonorrhoeae strains. Cross-resistance with other antibiotics was not detected. Mupirocin, which is currently limited to topical administration, demonstrated activity by injection when delivered in nanoliposomes. The nanoliposomal formulation of mupirocin is a potential treatment for drug-resistant N. gonorrhoeae .

Remote drug loading into nano-liposomes is in most cases the best method for achieving high concentrations of active pharmaceutical ingredients (API) per nano-liposome that enable therapeutically viable API-loaded nano-liposomes, referred to as nano-drugs. This approach also enables controlled drug release. Recently, we constructed computational models to identify APIs that can achieve the desired high concentrations in nano-liposomes by remote loading. While those previous models included a broad spectrum of experimental conditions and dealt only with loading, here we reduced the scope to the molecular characteristics alone. We model and predict API suitability for nano-liposomal delivery by fixing the main experimental conditions: liposome lipid composition and size to be similar to those of Doxil® liposomes. On that basis, we add a prediction of drug leakage from the nano-liposomes during storage. The latter is critical for having pharmaceutically viable nano-drugs. The "load and leak" models were used to screen two large molecular databases in search of candidate APIs for delivery by nano-liposomes. The distribution of positive instances in both loading and leakage models was similar in the two databases screened. The screening process identified 667 molecules that were positives by both loading and leakage models (i.e., both high-loading and stable). Among them, 318 molecules received a high score in both properties and of these, 67 are FDA-approved drugs. This group of molecules, having diverse pharmacological activities, may be the basis for future liposomal drug development.

Previously we have developed and statistically validated Quantitative Structure Property Relationship (QSPR) models that correlate drugs’ structural, physical and chemical properties as well as experimental conditions with the relative efficiency of remote loading of drugs into liposomes (Cern et al, Journal of Controlled Release, 160(2012) 14–157). Herein, these models have been used to virtually screen a large drug database to identify novel candidate molecules for liposomal drug delivery. Computational hits were considered for experimental validation based on their predicted remote loading efficiency as well as additional considerations such as availability, recommended dose and relevance to the disease. Three compounds were selected for experimental testing which were confirmed to be correctly classified by our previously reported QSPR models developed with Iterative Stochastic Elimination (ISE) and k-nearest neighbors (kNN) approaches. In addition, 10 new molecules with known liposome remote loading efficiency that were not used in QSPR model development were identified in the published literature and employed as an additional model validation set. The external accuracy of the models was found to be as high as 82% or 92%, depending on the model. This study presents the first successful application of QSPR models for the computer-model-driven design of liposomal drugs.

Mupirocin was identified by quantitative structure property relationship models as a good candidate for remote liposomal loading. Mupirocin is an antibiotic that is currently restricted to topical administration because of rapid hydrolysis in vivo to its inactive metabolite. Formulating mupirocin in PEGylated nanoliposomes may potentially expand its use to parenteral administration by protecting it from degradation in the circulation and target it (by the enhanced permeability effect) to the infected tissue. Mupirocin is slightly soluble in aqueous medium and its solubility can be increased using solubilizing agents. The effect of the solubilizing agents on mupirocin remote loading was studied when the solubilizing agents were added to the drug loading solution. Propylene glycol was found to increase mupirocin loading, whereas polyethylene glycol 400 showed no effect. Hydroxypropyl-β-cyclodextrin (HPCD) showed a concentration-dependent effect on mupirocin loading; using the optimal HPCD concentration increased loading, but higher concentrations inhibited it. The inclusion of HPCD in the liposome aqueous phase while forming the liposomes resulted in increased drug loading and substantially inhibited drug release in serum.

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R 2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments.