Clofazimine 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.
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.
The effect of solvent on salicylamide's crystal habit was investigated. It is deduced that ethyl acetate is adsorbed more strongly on the faces, the increased size of which, can explain the shape change.
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. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.
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