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Chemical Structure| 22071-15-4 Chemical Structure| 22071-15-4
Chemical Structure| 22071-15-4

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CAS No.: 22071-15-4

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Ketoprofen is a dual COX1/2 inhibitor with IC50 of 0.5 μM and 2.33 μM for human recombinant COX-1 and COX-2, respectively, and used as a nonsteroidal anti-inflammatory drug (NSAID) to treat arthritis-related inflammatory pains.

Synonyms: RP-19583; 2-(3-benzoylphenyl)Propionic Acid; (R,S)-Ketoprofen

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Product Citations

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García, Mauricio A ; Al-Gousous, Jozef ; González, Pablo M ; Langguth, Peter ;

Abstract: Drug product development is often a challenging endeavor. However, model-supported dissolution test design trained by appropriate in silico models can lead to considerable reduction in the risk of failure. Dissolution models have revealed that dissolution of poorly soluble ionizable pharmaceutical particles is slower in biorelevant bicarbonate than compendial buffers. The reason is bicarbonate’s lowered effective pKa (pka,eff), as a consequence of comparable reaction and diffusional times during dissolution. This is not necessarily the case when the drug is formulated as a controlled-release dosage form. In this paper, we explored the differences in dissolution between enteric coated (EC) and extended release (XR) ketoprofen formulations. In vitro dissolution was studied in low molarity buffers to mimic the lowered intestinal bicarbonate pka,eff, while their biorelevance was confirmed through in vivo comparative bioavailability studies. Both dissolution in low molarity phosphate and in vivo absorption profiles of EC tablets were sensitive to their coating polymer material. Similarly, XR in vitro dissolution in low molarity media showed discrepancies between formulations, caused by dibasic calcium phosphate in one formulation. Conversely, those in vitro differences were not relevant after the in vivo testing. Mechanistic insights from mass/charge balance modelling suggested that slower diffusional times and small liquid-to-solid ratio in XR dosage forms allow bicarbonate reactions to reach their equilibrium. This results in an enhanced buffer capacity, which was not matched by in vitro low molarity media. Therefore, improvement in biopredictivity of XR dosage forms can be achieved by performing dissolution experiments at high rather than low buffer molarities.

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Armen G. Beck ; Jonathan Fine ; Pankaj Aggarwal ; Erik L. Regalado ; Dorothy Levorse ; Jordan de Jesus Silva , et al.

Abstract: The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on different molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models employed to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set of 67,950 Merck proprietary analytes, these models achieved coefficients of determination of 0.84 and 3% error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN’s SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

Keywords: Liquid Chromatography ; Retention Time Prediction ; Support Vector Regression ; Gaussian Mixture Models ; Uniform Manifold Approximation & Projection

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Wegner, Scott A ; Kim, Hahn ; Avalos, José L ;

Abstract: Lactate transport plays a crucial role in the metabolism, microenvironment, and survival of cancer cells. However, current drugs targeting either MCT1 or MCT4, which traditionally mediate lactate import or efflux respectively, show limited efficacy beyond in vitro models. This limitation partly arises from the existence of both isoforms in certain tumors, however existing high-affinity MCT1/4 inhibitors are years away from human testing. Therefore, we conducted an optogenetic drug screen in Saccharomyces cerevisiae on a subset of the FDA-approved drug library to identify existing scaffolds that could be repurposed as monocarboxylate transporter (MCT) inhibitors. Our findings show that several existing drug classes inhibit MCT1 activity, including non-steroidal estrogens, non-steroidal anti-inflammatory drugs (NSAIDs), and natural products (in total representing approximately 1% of the total library, 78 out of 6400), with a moderate affinity (IC50 1.8–21 μM). Given the well-tolerated nature of NSAIDs, and their known anticancer properties associated with COX inhibition, we chose to further investigate their MCT1 inhibition profile. The majority of NSAIDs in our screen cluster into a single large structural grouping. Moreover, this group is predominantly comprised of FDA-approved NSAIDs, with seven exhibiting moderate MCT1 inhibition. Since these molecules form a distinct structural cluster with known NSAID MCT4 inhibitors, such as diclofenac, ketoprofen, and indomethacin, we hypothesize that these newly identified inhibitors may also inhibit both transporters. Consequently, NSAIDs as a class, and piroxicam specifically (IC50 4.4 μM), demonstrate MCT1 inhibition at theoretically relevant human dosages, suggesting immediate potential for standalone MCT inhibition or combined anticancer therapy.

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Tonduru, Arun Kumar ; Maljaei, Seyed Hamed ; Adla, Santosh Kumar ; Anamea, Landry ; Tampio, Janne ; Kralova, Adela , et al.

Abstract: OATP1C1 (organic anion-transporting polypeptide 1C1) transports thyroid hormones, particularly thyroxine (T4), into human astrocytes. In this study, we investigated the potential of utilizing OATP1C1 to improve the delivery of anti-inflammatory drugs into glial cells. We designed and synthesized eight novel prodrugs by incorporating T4 and 3,5-diiodo-L-tyrosine (DIT) as promoieties to selected anti-inflammatory drugs. The prodrug uptake in OATP1C1-expressing human U-87MG glioma cells demonstrated higher accumulation with T4 promoiety compared to those with DIT promoiety or the parent drugs themselves. In silico models of OATP1C1 suggested dynamic binding for the prodrugs, wherein the pose changed from vertical to horizontal. The predicted binding energies correlated with the transport profiles, with T4 derivatives exhibiting higher binding energies when compared to prodrugs with a DIT promoiety. Interestingly, the prodrugs also showed utilization of oatp1a4/1a5/1a6 in mouse primary astrocytes, which was further supported by docking studies and a great potential for improved brain drug delivery.

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Product Details of Ketoprofen

CAS No. :22071-15-4
Formula : C16H14O3
M.W : 254.28
SMILES Code : C2=C(C(C1=CC=CC=C1)=O)C=CC=C2C(C(O)=O)C
Synonyms :
RP-19583; 2-(3-benzoylphenyl)Propionic Acid; (R,S)-Ketoprofen
MDL No. :MFCD00055790
InChI Key :DKYWVDODHFEZIM-UHFFFAOYSA-N
Pubchem ID :3825

Safety of Ketoprofen

GHS Pictogram:
Signal Word:Danger
Hazard Statements:H301-H315-H319-H335
Precautionary Statements:P261-P301+P310-P305+P351+P338
Class:6.1
UN#:2811
Packing Group:Ⅲ
 

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