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, 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

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 dataset 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 five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 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

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

IntroductionOsteoarthritis 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.MethodsSix 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.ResultsCBD 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.ConclusionLiposomal-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

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 MIC 90 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

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

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, 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