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[ CAS No. 22071-15-4 ] {[proInfo.proName]}

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Chemical Structure| 22071-15-4
Chemical Structure| 22071-15-4
Structure of 22071-15-4 * Storage: {[proInfo.prStorage]}

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Quality Control of [ 22071-15-4 ]

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Product Citations

Product Citations      Expand+

Armen G. Beck ; Jonathan Fine ; Pankaj Aggarwal , et al. DOI:

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 DOI: PubMed ID:

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 , et al. DOI: PubMed ID:

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 [ 22071-15-4 ]

CAS No. :22071-15-4 MDL No. :MFCD00055790
Formula : C16H14O3 Boiling Point : -
Linear Structure Formula :HO2CCH(CH3)C6H4C(O)C6H5 InChI Key :DKYWVDODHFEZIM-UHFFFAOYSA-N
M.W : 254.28 Pubchem ID :3825
Synonyms :
RP-19583;2-(3-benzoylphenyl)Propionic Acid;(R,S)-Ketoprofen;(±)-Ketoprofen
Chemical Name :2-(3-Benzoylphenyl)propanoic acid

Calculated chemistry of [ 22071-15-4 ]      Expand+

Physicochemical Properties

Num. heavy atoms : 19
Num. arom. heavy atoms : 12
Fraction Csp3 : 0.12
Num. rotatable bonds : 4
Num. H-bond acceptors : 3.0
Num. H-bond donors : 1.0
Molar Refractivity : 72.67
TPSA : 54.37 ?2

Pharmacokinetics

GI absorption : High
BBB permeant : Yes
P-gp substrate : No
CYP1A2 inhibitor : No
CYP2C19 inhibitor : No
CYP2C9 inhibitor : No
CYP2D6 inhibitor : No
CYP3A4 inhibitor : No
Log Kp (skin permeation) : -5.64 cm/s

Lipophilicity

Log Po/w (iLOGP) : 1.91
Log Po/w (XLOGP3) : 3.12
Log Po/w (WLOGP) : 3.11
Log Po/w (MLOGP) : 2.69
Log Po/w (SILICOS-IT) : 3.37
Consensus Log Po/w : 2.84

Druglikeness

Lipinski : 0.0
Ghose : None
Veber : 0.0
Egan : 0.0
Muegge : 0.0
Bioavailability Score : 0.56

Water Solubility

Log S (ESOL) : -3.59
Solubility : 0.066 mg/ml ; 0.00026 mol/l
Class : Soluble
Log S (Ali) : -3.93
Solubility : 0.0298 mg/ml ; 0.000117 mol/l
Class : Soluble
Log S (SILICOS-IT) : -4.68
Solubility : 0.00535 mg/ml ; 0.000021 mol/l
Class : Moderately soluble

Medicinal Chemistry

PAINS : 0.0 alert
Brenk : 0.0 alert
Leadlikeness : 0.0
Synthetic accessibility : 2.57

Safety of [ 22071-15-4 ]

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

Application In Synthesis of [ 22071-15-4 ]

* All experimental methods are cited from the reference, please refer to the original source for details. We do not guarantee the accuracy of the content in the reference.

  • Downstream synthetic route of [ 22071-15-4 ]

[ 22071-15-4 ] Synthesis Path-Downstream   1~1

  • 1
  • [ 22071-15-4 ]
  • [ 125414-41-7 ]
  • Boc-serinol-ketoprofen [ No CAS ]
YieldReaction ConditionsOperation in experiment
87% Ketoprofen (1.11 mmol) (manufactured by Tokyo Kasei Kogyo) was dissolved in dichloromethane (3 ml), andtriethylamine (1.11 mmol) and dichloromethane solution (2 ml) of dimethylphosphinothioyl chloride (Mpt-Cl) (1.11 mmol)were added thereto in this order, followed by stirring for 25 minutes. Triethylamine (0.36 mmol) was further added thereto,followed by stirring for 20 minutes. The reaction solution was ice-cooled, and triethylamine (1.11 mmol), DMAP (0.19mmol) and the Boc-serinol obtained in Reference Example 2 (0.50 mmol) were added thereto in this order, followed bystirring overnight by returning to room temperature. The reaction solution was again ice-cooled, and 25% aqueousammonia (2 ml) and dioxane (10 ml) were added thereto in this order, followed by stirring for 20 minutes. The reactionsolution was concentrated to 5 ml, and ethyl acetate was added thereto. Separation by washing with water, 5% aqueouscitric acid solution, 5% aqueous sodium hydrogen carbonate solution and saturated brine consecutively was carried out,and after dehydration drying with sodium sulfate the solvent was evaporated under reduced pressure. The precipitatewas purified by silica gel column chromatography (hexane:ethyl acetate = 2:1, 0.5% triethylamine) to give the titledcompound (287.3 mg, yield 87%). The structure was identified by 1H-NMR (CDCl3). 1H-NMR (500 MHz, CDCl3) delta (ppm)= 1.38-1.40 (9H, m, Boc), 1.51-1.53 (6H, m, - OCOCH(CH3)-), 3.76-3.81 (2H, m, -OCOCH(CH3)-), 3.96-4.11 (4H, m, -CH2CH(NHBoc)CH2-), 4.61 (1H, btd, -CH2CH(NHBoc)CH2-), 7.40-7.80 (18H, m, Aromatic)
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