Role of biorelevant dissolution media in the selection of optimal Salt forms of oral drugs: maximizing the gastrointestinal solubility and in vitro activity of the antimicrobial molecule, clofazimine

peer-reviewedClofazimine is an antimycobacterial agent that is routinely used for the treatment of leprosy. Clofazimine has also been shown to have high clinical potential for the treatment of many Gram-positive pathogens, including those that exhibit high levels of antibiotic resistance in the medical community. The use of clofazimine against these pathogens has largely been limited by the inherently poor water solubility of the drug substance. In this work, the possibility of repurposing and reformulating clofazimine to maximize its clinical potential is investigated. To achieve this, the potential of novel salt forms of clofazimine as supersaturating drug delivery vehicles to enhance the aqueous solubility and gastrointestinal solubility of the drug substance was explored. The solution properties of seven novel salt forms, identified during an initial screening process, were examined in water and in a gastrointestinal-like media and were compared and contrasted with those of the free base, clofazimine, and the commercial formulation of the drug, Lamprene. The stability of the most promising solid forms was tested, and their bioactivity against Staphylococcus aureus was also compared with that of the clofazimine free base and Lamprene. Salts forms which showed superior stability as well as solubility and activity to the commercial drug formulation were fully characterized using a combination of spectroscopic techniques, including X-ray diffraction, solid-state NMR, and Fourier transform infrared spectroscopy

Solid and solution properties of the antimicrobial agent clofazimine

One strategy to combat antimicrobial resistant, a looming global threat, is to repurpose existing drug substances to fill the antibiotic discovery void until new treatments become available. Clofazimine is a hydrophobic antimicrobial agent which shows promise in vitro against most Gram-positive bacteria, including multidrug-resistant strains of Mycobacterium tuberculosis, Clostridium difficile and Staphylococcus aureus. However, the clinical use of clofazimine is hampered by low solubility, which results in a poor correlation between in vitro antimicrobial activity and in vivo success. The aim of this project was to study the solid and solution properties of clofazimine and optimise these properties to formulate the drug into a more effective antimicrobial agent. In the first phase of this project, a systematic characterisation of the solution and solid states properties of the two known polymorphs of clofazimine was carried out and this study led to the discovery of two novel polymorphs, one of which was the most thermodynamically stable under ambient conditions. None of these polymorphic forms of clofazimine displayed any detectable water solubility. The large hydrophobic skeleton of CFZ made solvation in water challenging, and it was established that only CFZ in the protonated state could improve water solubility. Thus, the use of pharmaceutical salts of clofazimine as supersaturating drug delivery systems was investigated. By screening potential salt forms in biorelevant dissolution media, the optimal salt form could be identified. This study resulted in the identification of several novel salts of CFZ, three of which displayed improved solution behaviour in biorelevant media compared to the existing commercial formulation, adequate long-term stability, and better in vitro antimicrobial activity compared to the free base. Despite the improvements in solution behaviour obtained from the new salt forms, the protonated clofazimine species obtained from their dissociation in biorelevant media exhibited poor solution stability and rapidly precipitated from solution following administration. This was found to be due to the common ion effect at low pH and deprotonation at higher pH. During the development of the salt screening protocol, certain amphipathic elements of the biorelevant media were noted to have a significant influence on the solution concentration of clofazimine. Thus, the effects of the various components of the biorelevant media on the solution behaviour of clofazimine were studied. Here, it was observed that the presence of bile acids, phospholipid and the digestive protein pepsin could increase the solution concentration of clofazimine. The presence of bile acids, phospholipid and pepsin also increased the induction time of clofazimine hydrochloride in low pH media, thus affecting the crystallisation kinetics of the salt. It was discovered that the digestive enzyme pepsin has a strong affinity for clofazimine molecules, which could overcome the common ion effect in the low pH gastric system and solubilise the API in the higher pH intestinal media. Pepsin was shown to solubilise clofazimine under gastric conditions and deliver the drug to the lower intestinal system, the site of C. difficile infections. The effectiveness of this enzyme-mediated drug delivery system was demonstrated in vitro in a dynamic dissolution system as well as in bioactivity assays against C. difficile

Re-envisioning the Design of Nanomedicines: Harnessing Automation and Artificial Intelligence

Introduction Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines. Areas covered This article highlights the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods. Expert opinion The development of integrated workflows based on automated experiments and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems

Overcoming the common ion effect for weakly basic drugs: inhibiting the crystallization of clofazimine hydrochloride in simulated gastrointestinal media

Bile salts, phospholipids, and digestive proteins are amphipathic compounds found naturally in the human gastrointestinal system. Therefore, it is important to consider their effects on the crystallization kinetics and solution behavior of drugs intended for oral delivery. Supersaturating drug delivery systems that employ high energy solid forms and polymeric additives are often hailed as the gold standard for increasing drug concentration in the gastrointestinal system. However, the effects of amphiphilic compounds present in the gastrointestinal system on the crystallization behavior of these systems are often overlooked. In this study, the effects of bile salts, phospholipids, mixtures of phospholipids and bile salts as well as digestive proteins on the crystallization kinetics of the antimicrobial agent clofazimine were evaluated. The crystallization inhibitory properties of these gastrointestinal amphiphiles were compared with commonly used synthetic polymers, and several of these amphipathic gastrointestinal compounds showed promise as crystallization inhibitors of clofazimine hydrochloride during induction time experiments. The best crystallization inhibitors from this induction time screening were then compared as solid physical mixtures in modified-fasted state simulated gastric fluid. Here it was found that heterogeneous nucleation of clofazimine hydrochloride occurred onto the dissolving surface of the administered clofazimine solid forms, preventing the various gastrointestinal compounds from inhibiting crystallization in this biorelevant media. This heterogeneous nucleation of clofazimine hydrochloride was monitored in real time, using optical microscopy techniques

Self-Driving Laboratories: A Paradigm Shift in Nanomedicine Development

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial in part due to the complexity associated with their preclinical development. Herein we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next generation nanomedicines, but also encourage participation of the pharmaceutical science community in a large data curation initiative

Data evaluating triamcinolone acetonide and triamcinolone hexacetonide loaded poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles

Advanced drug delivery strategies can be used to enhance the therapeutic effectiveness of locally delivered corticosteroids. Poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles (PVL-co-PAVL MPs) were evaluated for delivery of two corticosteroids, triamcinolone acetonide and triamcinolone hexacetonide. PVL-co-PAVL MPs were prepared using a modified oil-in-water emulsification method, followed by a UV-initiated cross-linking process. The resulting PVL-co-PAVL MPs were purified with an excess amount of water and then acetone to remove residual surfactant, cross-linker, and catalyst before lyophilization. Triamcinolone acetonide and triamcinolone hexacetonide were independently loaded into the resulting PVL-co-PAVL MPs via a post-loading swelling-equilibrium method. The drug-loaded MPs were characterized in terms of drug loading (determined by high-performance liquid chromatography, HPLC), thermal properties (determined by differential scanning calorimetry, DSC), and in vitro drug release kinetics (with quantification of drug using HPLC) to better understand the suitability of PVL-co-PAVL MPs for delivery of corticosteroids. These data demonstrate the potential of PVL-co-PAVL MPs as a promising drug delivery platform for the sustained release of corticosteroids. Raw data have been made available on Mendeley Data. Additional details on PVL-co-PAVL MPs were previously reported [1]

Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration often remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., estimated to be more than 1200 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevent drug degradation. Moreover, our high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®

Machine learning models to accelerate the design of polymeric long-acting injectables

Polymer-based long-acting injectable drugs are a promising therapeutic strategy for chronic diseases. Here the authors use machine learning to inform the data-driven development of advanced drug formulations

Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles. Analysis of the best performing model uncovered the properties of the drug and polymer that were identified to be key determinants of drug release. This information can be used to identify promising drug-polymer combinations that result in long-acting injectables with specific drug release behaviour. The implementation of this data-driven approach has the potential to reduce the time and cost associated with formulation development. Datasets and relevant codes used to train the machine learning models have been made openly available to encourage usage in future drug formulation efforts

Investigation into the Solid and Solution Properties of Known and Novel Polymorphs of the Antimicrobial Molecule Clofazimine

Clofazimine is an anti-mycobacterial agent used as part of a multidrug treatment for leprosy. Recently clofazimine has shown promising activity against multidrug resistant tuberculosis. Clofazimine has been previously known to exist in two different crystal forms, or polymorphs, which are triclinic (F I) and monoclinic (F II) in crystal structure. The thermodynamic relationship between, and the solubility of, these different crystal structures of clofazimine has not previously been characterized. In this work, their solid and solution properties are studied, and as a result, two novel polymorphs of clofazimine (an orthorhombic crystal polymorph and a high temperature polymorph with a monoclinic structure) are reported. The properties of these new solid forms are compared and contrasted with those of the two previously reported polymorphs using thermal, spectroscopic, and microscopic techniques. Molecular modeling studies were also carried out to predict the relative thermodynamic relationship and the crystal morphology of the polymorphs. There was an excellent correlation observed between the aforementioned experimental and molecular modeling results, allowing for the unequivocal determination of the thermodynamic relationship between all four polymorphs of clofazimine