Two Possible Strategies for Drug Modification of Gemcitabine and Future Contributions to Personalized Medicine

Drug repurposing is an emerging strategy, which uses already approved drugs for new medical indications. One such drug is gemcitabine, an anticancer drug that only works at high doses since a portion is deactivated in the serum, which causes toxicity. In this review, two methods were discussed that could improve the anticancer effect of gemcitabine. The first is a chemical modification by conjugation with cell-penetrating peptides, namely penetratin, pVEC, and different kinds of CPP6, which mostly all showed an increased anticancer effect. The other method is combining gemcitabine with repurposed drugs, namely itraconazole, which also showed great cancer cell inhibition growth. Besides these two strategies, physiologically based pharmacokinetic models (PBPK models) are also the key for predicting drug distribution based on physiological data, which is very important for personalized medicine, so that the correct drug and dosage regimen can be administered according to each patient’s physiology. Taking all of this into consideration, it is believed that gemcitabine can be repurposed to have better anticancer effects.

CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method

Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

CASTELO: Clustered Atom Subtypes Aided Lead Optimization - A Combined Machine Learning and Molecular Modeling Method

Background: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots.Results: The initial data collection is achieved with physics-based molecular dynamics (MD) simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization.Conclusion: With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

A Recent Review on Drug Modification Using 1,2,3-triazole

Motivated by evidence garnered from literature probing the use of triazoles in drug discovery and development, we reported the utilization of bioisosteric replacement and molecular hybridization in this review. Bio-isosteric replacement has played a significant role in modulating rapid and versatile strategy in synthesizing molecules with multifaceted medicinal properties. Molecular hybridization seeks to conjugate two molecular fragments with diverse applications under very mild reaction conditions. In this regard, 1,2,3-triazole is a well-known scaffold with widespread occurrence in medicinal compounds. It is characterized to have several bioactivities such as anti-microbial, anti-cancer, anti-viral, analgesic, anti- inflammatory effects. Furthermore, the structural features of 1,2,3-triazoles enable it to mimic different functional groups justifying its use as bio-isostere for the synthesis of new molecules of medicinal interest, which we have reported briefly.

Ciprofloxacin and Graphene Oxide Combination—New Face of a Known Drug

Drug modification with nanomaterials is a new trend in pharmaceutical studies and shows promising results, especially considering carbon-based solutions. Graphene and its derivatives have attracted much research interest for their potential applications in biomedical areas as drug modifiers. The following work is a comprehensive study regarding the toxicity of ciprofloxacin (CIP) modified by graphene oxide (GO). The influence on the morphology, viability, cell death pathway and proliferation of T24 and 786-0 cells was studied. The results show that ciprofloxacin modified with graphene oxide (CGO) shows the highest increase in cytotoxic potential, especially in the case of T24 cells. We discovered a clear connection between CIP modification with GO and the increase in its apoptotic potential. Our results show that drug modification with carbon-based nanomaterials might be a promising strategy to improve the qualities of existing drugs. Nevertheless, it is important to remember that cytotoxicity effects are highly dependent on dose and nanomaterial size. It is necessary to conduct further research to determine the optimal dose of GO for drug modification.

PHARMACEUTICO - ANALYTICAL STANDARDIZATION OF AJAMODADI CHOORNA AND ITS DRUG MODIFICATION AS AJAMODADI CHEWABLE TABLET

Ayurveda is ancient science. Acharya motioned chatushpada which is bhishak, dravya, rogi, upstatha. In which dravya means ayurvedic medicine Standardization of herbal formulations is important to calculate the quality of drugs, based on the concentration of their active principles. Ajamodadi Choorna is a classical formulation mentioned in Bhaisajyaratnavali with the indication Swarbheda This article highlights on pharmaceutico - analytical standardization of ajamodadi choorna and its drug modification as Ajamodadi chewable tablet standardization of Ajmodadi churna.

Ionic Liquids as Potential and Synergistic Permeation Enhancers for Transdermal Drug Delivery

Transdermal drug delivery systems (TDDS) show clear advantages over conventional routes of drug administration. Nonetheless, there are limitations to current TDDS which warrant further research to improve current TDD platforms. Spurred by the synthesis of novel biodegradable ionic liquids (ILs) and favorable cytotoxicity studies, ILs were shown to be a possible solution to overcome these challenges. Their favorable application in overcoming challenges ranging from synthesis, manufacture, and even therapeutic benefits were documented. In this review, said ILs are highlighted and their role in TDDS is reviewed in terms of (a) ILs as permeation enhancers (single agents or combined), (b) ILs in drug modification, and (c) ILs as active pharmaceutical ingredients. Furthermore, future combination of ILs with other chemical permeation enhancers (CPEs) is proposed and discussed.