Publications by authors named "Melike Cokol-Cakmak"

Changes in weather conditions and lifestyle lead to an annual increase in the amount of lung cancer, and therefore it is one of the three most common types of cancer, making it important to find an appropriate treatment method. This research aims to introduce a new smart nano-drug delivery system with antibacterial and anticancer capabilities that could be applied for the treatment of lung cancer. It is composed of a niosomal carrier containing curcumin as an anticancer drug and is coated with a chitosan polymeric shell, alongside Rose Bengal (RB) as a photosensitizer with an antibacterial feature.

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With the development of nanotechnology, various drug delivery systems including inorganic nanoparticles, liposomes, polymers, etc. have been developed over the past decade. Some of these nanoparticles are also forthcoming candidates for the successful delivery of small interfering RNA (siRNA) for targeted gene silencing.

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Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations.

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Drugs may have synergistic or antagonistic interactions when combined. Checkerboard assays, where two drugs are combined in many doses, allow sensitive measurement of drug interactions. Here, we describe a protocol to measure the pairwise interactions among three antibiotics, in duplicate, in 5 days, using only two 96-well microplates and standard laboratory equipment.

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A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs.

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Small interfering RNAs (siRNA) are short nucleic acid fragments of about 20-27 nucleotides, which can inhibit the expression of specific genes. siRNA based RNAi technology has emerged as a promising method for the treatment of a variety of diseases. However, a major limitation in the therapeutic use of siRNA is its rapid degradation in plasma and cellular cytoplasm, resulting in short half-life.

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Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth.

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