Validation of an UV-Visible spectrophotometry assay method for the determination of chlorpheniramine maleate tablets without prior extraction

Methods proposed by pharmacopeias to check the quality of chlorpheniramine maleate tablets are multi steps methods which involve extraction and present issues with repeatability. An alternative method is proposed with a sensitive, reliable, simple and rapid UV-VISIBLE spectrophotometry method developed for the determination without extraction of chlorpheniramine maleate in tablets. The method was validated using the accuracy profile approach with an accuracy ranging from 99.70 to 100.46%. Analysis was done using 0.25 mol/L sulfuric acid, distilled water, and raw material in a room at 23 °C for 25 to 30 minutes. Chlorpheniramine maleate concentration varied from 0.018 to 0.03 mg/mL. The method was found to be specific with the appearance of the corresponding maxima at 265 nm and a correlation coefficient (R2 ) of 0.9993. Limits of detection and quantification were respectively 1.39 × 10-4and 2.26 × 10-3 mg/mL. The tolerance limits for the different concentration levels (75, 100 and 125%) were respectively: 88.37; 92.74 and91.62% for lower limits and 111.03; 108.17 and 108.02% for upper limits. It was observed that the tolerance limits were within the limits of acceptability set at 20%. Consequently, the method was declared valid and reliable for routine analysis of chlorpheniramine maleate in tablets containing chlorphenamine as active principle.Keywords : Chlorpheniramine maleate, tablets, accuracy profile, assay method without extraction, validation

Penerapan domain knowledge pada model data nasabah perbankan dalam memprediksi churn rate

Penelitian ini mendiskusikan tentang proses pembuatan dan pengoptimalan model data yang digunakan untuk memprediksi kemungkinan keluarnya nasabah dari suatu layanan perbankan berdasarkan beberapa variabel independen yang disediakan oleh pihak perbankan. Penelitian ini menggunakan Regresi Logistik sebagai metode utama untuk menghasilkan model hubungan antar data yang menggambarkan klasifikasi umum nasabah yang akan keluar dari layanan perbankan. Penelitian ini menggunakan metode Backward Elimination dan penerapan domain knowledge untuk mengoptimasi kinerja dari model yang dihasilkan. Model terakhir yang dihasilkan akan dinilai kembali dengan metode Cumulative Accuracy Profile. Hasil dari penelitian ini memiliki tingkat akurasi yang baik dalam memprediksi kemungkinan keluarnya nasabah dari layanan perbankan. Model dapat memprediksi 60% orang yang akan keluar dari bank dengan memeriksa 29% dari keseluruhan data. Hasil keluaran penelitian ini dapat dikaitkan dengan tingkat efisiensi usaha yang dapat dilakukan oleh pihak perbankan dalam mengambil tindakan pada nasabah dengan tingkat probabilitas tertinggi untuk keluar dari layanan perbankan.

Development and validation of a UV-Visible method for the determination of the active principle Efavirenz in tablets

Methods proposed by pharmacopeias to control the quality of Efavirenz, an antiretroviral drug used in the treatment of AIDS are based on expensive equipment which are most of the time not available in African countries. To solve this issue, using UV-visible spectrophotometry, a cheap and easy-to-use device available in many laboratories on the African continent; a sensitive, reliable, simple and rapid method has been validated for the determination of the active substance Evafirenz in Efavirenz tablets, to control their quality by checking its dosage. The accuracy profile approach was adopted. The response function gave a correlation coefficient R2 = 0.9987. The detection and quantification limits were 0.15625 μg / mL and 0.515625 μg / mL, respectively. The acceptability limit has been set at 15%. The tolerance limits for the different concentration levels (80, 100 and 120%) are represented respectively as follows: lower limits 86.2; 90.3 and 96.1%; upper limits 100.2; 106.3 and 113.7%. The tolerance limits are within the acceptability limits for values around 100% concentration (7 μg / mL). Therefore, the method was declared valid and reliable for the analysis of Efavirenz in Efavirenz tablets and could routinely be used in laboratories for the quality control of EFAVIRENZ drug. © 2020 International Formulae Group. All rights reserved. Keywords: Efavirenz, accuracy profile, spectrophotometry, pharmaceutical formulation

Development and Validation of an in-line API Quantification Method Using AQbD Principles Based on UV-Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

A key principle of developing a new medicine is that quality should be built in, with a thorough understanding of the product and the manufacturing process supported by appropriate process controls. Quality by design principles that have been established for the development of drug products/substances can equally be applied to the development of analytical procedures. This paper presents the development and validation of a quantitative method to predict the concentration of piroxicam in Kollidon® VA 64 during hot melt extrusion using analytical quality by design principles. An analytical target profile was established for the piroxicam content and a novel in-line analytical procedure was developed using predictive models based on UV-Vis absorbance spectra collected during hot melt extrusion. Risks that impact the ability of the analytical procedure to measure piroxicam consistently were assessed using failure mode and effect analysis. The critical analytical attributes measured were colour (L* lightness, b* yellow to blue colour parameters—in-process critical quality attributes) that are linked to the ability to measure the API content and transmittance. The method validation was based on the accuracy profile strategy and ICH Q2(R1) validation criteria. The accuracy profile obtained with two validation sets showed that the 95% β-expectation tolerance limits for all piroxicam concentration levels analysed were within the combined trueness and precision acceptance limits set at ±5%. The method robustness was tested by evaluating the effects of screw speed (150–250 rpm) and feed rate (5–9 g/min) on piroxicam content around 15% w/w. In-line UV-Vis spectroscopy was shown to be a robust and practical PAT tool for monitoring the piroxicam content, a critical quality attribute in a pharmaceutical HME process.

A Boosted Decision Tree Model for Predicting Loan Default in P2P Lending Communities

Loan Default Prediction For Social Lending Is An Emerging Area Of Research In Predictive Analytics. The Need For Large Amount Of Data And Few Available Studies In The Current Loan Default Prediction Models For Social Lending Suggest That Other Viable And Easily Implementable Models Should Be Investigated And Developed. In View Of This, This Study Developed A Data Mining Model For Predicting Loan Default Among Social Lending Patrons, Specifically The Small Business Owners, Using Boosted Decision Tree Model. The United States Small Business Administration (Usba) PubliclyAvailable Loan Administration Dataset Of 27 Features And 899164 Data Instances Was Used In 80:20 Ratios For The Training And Testing Of The Model. 16 Data Features Were Finally Used As Predictors After Data Cleaning And Feature Engineering. The Gradient Boosting Decision Tree Classifier Recorded 99% Accuracy Compared To The Basic Decision Tree Classifier Of 98%. The Model Is Further Evaluated With (A) Receiver Operating Characteristics (Roc) And Area Under Curve (Auc), (B) Cumulative Accuracy Profile (Cap), And (C) Cumulative Accuracy Profile (Cap) Under Auc. Each Of These Model Performance Evaluation Metrics, Especially Roc-Auc, Showed The Relationship Between The True Positives And False Positives That Implies The Model Is A Good Fit.

Optimized DLLME Method Coupled to HPLC for Simultaneous Analyses of Benzoates, Sorbates and Methyl p-hydroxyl Benzoates in Fruits Products

We used an accurate and green technique “Dispersive Liquid Liquid Micro Extraction” (DLLME) followed by HPLC-UV analysis for the analysis of benzoates (BA), sorbates (SA), and p-hydroxyl methyl benzoates (MB) in fruits products. We optimized the extraction parameters by the box Behnken design. We validated the method by using the β accuracy profile including the systematic (trueness) and random (intermediate precision) errors. The extraction efficiency varied from 82.98 to 100.8 %. This method was linear with R2s values higher than 0.9752 for all the compounds. The repeatability and intermediate precision were less than 3.18 and 12.02 respectively; whereas, the detection limits were respectively 0.304, 0.306 and 0.153 mg/ ml for BA, SA and MB. This method is therefore reliable to the simultaneous quantification of these preservatives in fruit juices, nectars and jams. The levels of these additives in the samples exceeded 833.97 and 290.67 ppm for BA and SA, respectively; while, MB was not detected.