In Vitro Susceptibility of Neisseria gonorrhoeae Strains to Mupirocin, an Antibiotic Reformulated for Parenteral Administration in Nanoliposomes.

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.

Therapeutic efficacy and pharmacokinetics of liposomal-cannabidiol injection: a pilot clinical study in dogs with naturally-occurring osteoarthritis.

Introduction: Osteoarthritis is a common disease in dogs resulting in chronic pain and decreased wellbeing. Common analgesics such as non-steroidal anti-inflammatories may fail to control pain and can produce major adverse effects. Study objectives were to evaluate pharmacokinetics, therapeutic efficacy, and safety of subcutaneous liposomal-cannabidiol (CBD) as an additional analgesic therapy in dogs suffering from naturally-occurring osteoarthritis. Methods: Six such dogs were recruited following ethics approval and owner consent. Dogs were administered a single subcutaneous injection of 5 mg/kg liposomal-CBD. Plasma concentrations of CBD, blood work, activity monitoring collar data, wellbeing questionnaire (owners) and pain scoring (veterinarian) were performed at baseline and monitored up to six weeks following intervention. Data overtime were compared with baseline using linear-regression mixed-effects. P-value was set at 0.05. Results: CBD plasma concentrations were observed for 6 weeks; median (range) peak plasma concentration (Cmax) was 45.2 (17.8-72.5) ng/mL, time to Cmax was 4 (2-14) days and half-life was 12.4 (7.7-42.6) days. Median (range) collar activity score was significantly increased on weeks 5-6; from 29 (17-34) to 34 (21-38). Scores of wellbeing and pain evaluations were significantly improved at 2-3 weeks; from 69 (52-78) to 53.5 (41-68), and from 7.5 (6-8) to 5.5 (5-7), respectively. The main adverse effect was minor local swelling for several days in 5/6 dogs. Conclusion: Liposomal-CBD administered subcutaneously produced detectable CBD plasma concentrations for 6 weeks with minimal side effects and demonstrated reduced pain and increased wellbeing as part of multimodal pain management in dogs suffering from osteoarthritis. Further placebo-controlled studies are of interest.

A Case Report of Subcutaneously Injected Liposomal Cannabidiol Formulation Used as a Compassion Therapy for Pain Management in a Dog.

A 14-year-old intact mixed breed dog (26 kg) was submitted for a novel cannabidiol (CBD) analgesic treatment. The dog was cachectic and had a testicular neoplasia, hip and elbow osteoarthritis and severe cervical pain. Analgesic treatment included canine osteoarthritic supplement, robencoxib and gabapentin. An additional liposomal CBD injectable formulation at 5 mg/kg was administered subcutaneously between the shoulder blades. The dog was monitored using an activity monitoring collar (PetPace), owner wellbeing questionnaire (Canine Brief Pain Inventory; CBPI), pain interactive visual analog scale (iVAS), blood work and CBD plasma concentrations. A week from the injection and up to 3 weeks afterwards the dog had improved CBPI and iVAS pain scores, and increased collar activity scores. CBD was quantified in plasma for 28 days. Due to disease progression, further difficulty to rise and walk, and relapse to pain after 3 weeks, the owners requested a second liposomal CBD injection, which was performed 4 weeks following the first injection using 3 mg/kg dose. Two days later, the dog was found dead in the yard under direct sun, while environmental temperature was 37°C. Major findings on necropsy revealed evidence of heat stroke and severe cervical disc protrusion with spinal hematoma, none related to liposomal CBD. In conclusion, subcutaneous liposomal CBD produced quantifiable CBD plasma concentrations for 28 days and may be an effective additional treatment as part of multimodal pain management in dogs.

Therapeutic Potential of Injectable Nano-Mupirocin Liposomes for Infections Involving Multidrug-Resistant Bacteria.

Antibiotic resistance is a global health threat. There are a few antibiotics under development, and even fewer with new modes of action and no cross-resistance to established antibiotics. Accordingly, reformulation of old antibiotics to overcome resistance is attractive. Nano-mupirocin is a PEGylated nano-liposomal formulation of mupirocin, potentially enabling parenteral use in deep infections, as previously demonstrated in several animal models. Here, we describe extensive in vitro profiling of mupirocin and Nano-mupirocin and correlate the resulting MIC data with the pharmacokinetic profiles seen for Nano-mupirocin in a rat model. Nano-mupirocin showed no cross-resistance with other antibiotics and retained full activity against vancomycin-, daptomycin-, linezolid- and methicillin- resistant Staphylococcus aureus, against vancomycin-resistant Enterococcus faecium, and cephalosporin-resistant Neisseria gonorrhoeae. Following Nano-mupirocin injection to rats, plasma levels greatly exceeded relevant MICs for >24 h, and a biodistribution study in mice showed that mupirocin concentrations in vaginal secretions greatly exceeded the MIC90 for N. gonorrhoeae (0.03 µg/mL) for >24 h. In summary, Nano-mupirocin has excellent potential for treatment of several infection types involving multiresistant bacteria. It has the concomitant benefits from utilizing an established antibiotic and liposomes of the same size and lipid composition as Doxil®, an anticancer drug product now used for the treatment of over 700,000 patients globally.

Liposomal mupirocin holds promise for systemic treatment of invasive Staphylococcus aureus infections.

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.

New drug candidates for liposomal delivery identified by computer modeling of liposomes' remote loading and leakage.

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.

Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading.

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., J. Control. Release 160 (2012) 147-157). 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 by us 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.

Effect of solubilizing agents on mupirocin loading into and release from PEGylated nanoliposomes.

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-ß-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.

Quantitative structure-property relationship modeling of remote liposome loading of drugs.

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.