HPLC-DAD technique for the quantification of a recently approved anti-diabetic triple combination along with two toxic official impurities: Toxicity confirmation aided by molecular docking application

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Development of eco-friendly spectrophotometric methods for analysis of metformin hydrochloride and linagliptin in presence of metformin toxic impurity in their pure and dosage forms: Validation, practicality and greenness studies

Metformin is considered as type 2 diabetes first line treatment according to American Diabetes Association and European Association. But, in some cases, di- or tri - therapy should be prescribed for glycemic management, prevention of the maximum dose side effects and induced effectiveness. Co-administration of Linagliptin with metformin has many benefits on diabetic patients such as decrease the possibility of hypoglycemia. For the first time, novel and reliable techniques were developed and verified for the concurrent quantification of metformin hydrochloride and linagliptin, while accounting for the existence of metformin toxic impurity 1-cyanoguanidine in their pure and dosage forms. Method (A) utilizes the zero-order spectrophotometric approach to quantitatively determine the concentration of linagliptin. The measurements are performed at a wavelength of 295 nm. The double divisor derivative ratio spectrophotometric method is used in Method (B) to measure the amounts of metformin and cyanoguanidine at 252 nm and 219 nm wavelengths, respectively. The spectrophotometric method (C) for determining metformin and cyanoguanidine at 252 nm and 223 nm, respectively, is based on the single divisor derivative ratio-zero crossing technique. The obtained findings were subjected to statistical comparison with the reported method, revealing no statistically significant differences. The Green Analytical Procedure Index (GAPI) and Analytical GREEnness Metric approach (AGREE) determined that these approaches had a high degree of environmental friendliness. Additionally, the proposed strategy was deemed to be practical according to the Blue Applicability Grade Index (BAGI) assessment tool.

Exciting Advances in Sustainable Spectrophotometric Micro-Quantitation of an Innovative Painkiller "Tramadol and Celecoxib" Mixture in the Presence of a Toxic Impurity, Promoting Greenness and Whiteness Studies

BACKGROUND

Tramadol (TRM) and celecoxib (CLX) form a novel mixture that helps relieve acute pain when other painkillers have no action. It is also reported that these drugs, TRM and CLX, are used to control COVID-19 symptoms.

OBJECTIVE

The current work highlights three important pillars of modern pharmaceutical analysis, which are as follows; impurity profiling, greenness/whiteness studies and simplicity accompanied by sensitivity. Since 4-methyl acetophenone inhibits the human carbonyl reductase enzyme (type I) and since this compound may pose a health risk, it is crucial to regulate its concentration in all dosage forms of CLX.

METHODS

Two simple and green spectrophotometric methods were developed, namely third derivative (D3) and Fourier self- deconvulation (FSD), for resolving severely overlapped spectra of TRM and CLX in the presence of 4-methyl acetophenone (4-MAP) as a process-related impurity in their novel tablet combination.

RESULTS

The two approaches showed acceptable linearity with an excellent correlation coefficient. In both methods, TRM was measured when CLX and 4-methyl acetophenone were zero-crossing. The same procedure was applied for measuring CLX and its process-related impurity 4-MAP.

CONCLUSION

The methodologies developed were thoroughly validated in compliance with ICH (International Council on Harmonisation) guidelines. Student t- and F-tests revealed no statistically significant variation among the current methods and the reported method.

HIGHLIGHTS

No spectrophotometric methods have been published previously for the simultaneous analysis of TRM and CLX along with 4-MAP. As a result, the newly developed spectrophotometric approaches have great relevance and originality in the field of pharmaceutical analysis.

Sustainable Stability-Indicating spectra manipulations for the concurrent quantification of a novel Anti-COVID-19 drug and its active Metabolite: Green profile assessment

Millions of individuals have lost their lives and changed their routines as a direct consequence of exposure to the coronavirus (Covid-19). Molnupiravir (MOL) is an orally bioavailable tiny molecule antiviral prodrug that is effective for curing the coronavirus that produces serious acute respiratory disorder (SARS-CoV-2). Fully green-assessed stability-indicating simple spectrophotometric methods have been developed and fully validated as per ICH criteria. The potential impact of degradation products of drug components on the safety and efficacy of a medication's shelf life is likely to be negligible. The field of pharmaceutical analysis necessitates various stability testing under different conditions. The conduct of such inquiries affords the prospect of predicting the most probable routes of degradation and ascertaining the inherent stability characteristics of the active drugs. Consequently, a surge in demand arose for the creation of an analytical methodology that could consistently measure the degradation products and/or impurities that may be present in pharmaceuticals. Herein, five smart and simple spectrophotometric data manipulation techniques have been produced for the concurrent estimation of MOL and its active metabolite as its possible acid degradation product namely; N-hydroxycytidine (NHC). Structure confirmation of NHC build-up through IR, MS and NMR analyses. All current techniques verified linearity ranging from 10 to 150 μg/ml and 10-60 μg/ml for MOL and NHC, respectively. The limit of quantitation (LOQ) values were in the range of 4.21-9.59 μg/ml, while the limit of detection (LOD) values were ranging from 1.38 - 3.16 μg/ml. The current methods were evaluated in terms of greenness by four assessing methods and confirmed to be green. The significant novelty of these methods depends on their being the first environmentally soundness stability-indicating spectrophotometric approaches for the concurrent estimation of MOL and its active metabolite, NHC. Also, the preparation of purified NHC delivers significant cost savings, instead of purchasing an expensive ingredient. These smart methods were utilized for analyzing the pharmaceutical dosage form which may be of great benefit to the pharmaceutical market.