Exclusion of Objectionable Microorganisms from Non‐sterile Pharmaceutical Drug Products

Abstract Investigation of out-of-specification analytical results is laborious, time-consuming, and costly and must be well documented. However, an analytical result is not complete unless reported with its measurement uncertainty. Here, we compare four different approaches for measurement uncertainty estimation used in acetaminophen quantification in pharmaceutical drug products. Measurement uncertainties were estimated using a repeatability and reproducibility study, Eurachem/Citac guidelines, Monte Carlo simulations, and a spreadsheet method. These different approaches provided similar results. However, they differed by the sources of uncertainties considered, by the procedures of calculation, and by the effort required in routine applications. Nevertheless, all four approaches were successful in assessing conformity of acetaminophen content in pharmaceutical drug products and may be used in assessing pharmaceutical equivalence.

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MOLECULAR CRYSTALLIZATION IN MICROFLUIDIC DROPLET ENSEMBLES – TOWARDS DESIGNER PHARMACEUTICAL DRUG PRODUCTS

10.3390/optofluidics2017-04504 ◽

2017 ◽

Author(s):

Saif A Khan

Keyword(s):

Pharmaceutical Drug ◽

Drug Products ◽

Molecular Crystallization

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Predicting the environmental emissions arising from conventional and nanotechnology-related pharmaceutical drug products

Environmental Research ◽

10.1016/j.envres.2020.110219 ◽

2021 ◽

Vol 192 ◽

pp. 110219

Author(s):

Fabian Jung ◽

Manuela Thurn ◽

Katharina Krollik ◽

Ge Fiona Gao ◽

Indra Hering ◽

...

Keyword(s):

Pharmaceutical Drug ◽

Drug Products ◽

Environmental Emissions

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Risk Assessment, Screening and Control of Elemental Impurities in Pharmaceutical Drug Products: A Review

Current Pharmaceutical Analysis ◽

10.2174/1573412915666190314144134 ◽

2020 ◽

Vol 16 (7) ◽

pp. 801-805

Author(s):

Rajesh Kumar Chawla ◽

Subhranshu Panda ◽

Kulandaivelu Umasankar ◽

Siva Prasad Panda ◽

Dalu Damayanthi

Keyword(s):

Risk Assessment ◽

Inductively Coupled Plasma ◽

Optical Emission ◽

The United States ◽

Emission Spectrometry ◽

Exposure Limits ◽

Pharmaceutical Drug ◽

Inductively Coupled ◽

Drug Products ◽

Elemental Impurities

This article describes and reviews the steps involved in risk assessment of the twenty-four (24) potential elemental impurities in pharmaceutical drug products, as per the permitted daily exposure limits. Screening and estimation of prescribed elemental impurities in pharmaceutical drug substances, inactive excipients and drug products by inductively coupled plasma mass spectrometry or inductively coupled plasma optical emission spectrometry and their controls involved are also reviewed, as referred in the general chapters <232> & <233> of the United States Pharmacopoeia, Q3D guideline for elemental impurities as per international conference on harmonization and q3d elemental impurities: guidance for industry as per U. S., Food and Drug Administration USFDA.

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Evaluation of key sources of variability in the measurement of pharmaceutical drug products by near infrared reflectance spectroscopy

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/s0731-7085(97)00229-x ◽

1998 ◽

Vol 17 (4-5) ◽

pp. 641-650 ◽

Cited By ~ 15

Author(s):

Matthew W Borer ◽

Xiangji Zhou ◽

Donna M Hays ◽

Jeffrey D Hofer ◽

Kevin C White

Keyword(s):

Near Infrared ◽

Reflectance Spectroscopy ◽

Infrared Reflectance ◽

Near Infrared Reflectance Spectroscopy ◽

Near Infrared Reflectance ◽

Pharmaceutical Drug ◽

Infrared Reflectance Spectroscopy ◽

Drug Products

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A review and analysis of product recall for pharmaceutical drug product

Journal of Generic Medicines The Business Journal for the Generic Medicines Sector ◽

10.1177/17411343211033887 ◽

2021 ◽

pp. 174113432110338

Author(s):

Ashish Miglani ◽

Chandan Saini ◽

Pankaj Musyuni ◽

Geeta Aggarwal

Keyword(s):

Drug Product ◽

Pharmaceutical Product ◽

Regulatory Agencies ◽

Primary Concern ◽

Product Recalls ◽

Product Recall ◽

High Quality ◽

Pharmaceutical Drug ◽

Drug Products ◽

Digital World

The pharmaceutical industry's primary concern is to provide high-quality drug products to the general public, so drug recalls play an important role in maintaining the quality system by removing defective products from the market. Pharmaceutical product recalls are increasing at an alarming rate as a result of increased inspection rates and the introduction of modernization and the digital world into the industry, raising concerns for regulatory agencies and public health to focus on more stringent regulations to control future recalls of defective drug products. This article will provide an overview of recall procedures, their impact on the pharmaceutical industry, and the various steps taken to reduce pharmaceutical recalls.

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Segmentasi Pelanggan Ritel Produk Farmasi Obat Menggunakan Metode Data Mining Klasterisasi Dengan Analisis Recency Frequency Monetary (RFM) Termodifikasi

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020702925 ◽

2020 ◽

Vol 7 (3) ◽

pp. 573

Author(s):

Arief Wibowo ◽

Andy Rio Handoko

Keyword(s):

Data Mining ◽

Drug Product ◽

Pharmaceutical Products ◽

Pharmaceutical Product ◽

Customer Segmentation ◽

Selling Price ◽

Data Mining Algorithm ◽

Transaction Data ◽

Pharmaceutical Drug ◽

Drug Products

Secara umum, pembelian produk farmasi di Indonesia tidak memiliki pola. Pembelian produk farmasi seperti obat-obatan, dilakukan oleh individu bukan sebagai persiapan untuk menjaga kesehatan, namun sebagai respon terhadap penyakit yang sedang diderita. Di sisi lain, pelanggan retail produk farmasi obat biasanya dipengaruhi oleh faktor harga jual dan faktor kecocokan (sugesti) pada merk obat tertentu sewaktu melakukan pembelian. Berdasarkan kondisi itu maka pola pembelian obat bagi masyarakat Indonesia menjadi tidak dapat diprediksi. Hal tersebut membuat pelaku usaha di bisnis ritel produk farmasi obat, relatif sulit untuk meningkatkan nilai penjualan. Salah satu upaya yang bisa dilakukan pelaku bisnis untuk meningkatkan pendapatan adalah dengan melakukan promosi penjualan berdasarkan jenis kelompok pelanggannya. Transaksi pembelian produk farmasi obat dapat dianalisis untuk mengetahui segmentasi pelanggan berdasarkan pola pembelian. Riset ini telah berhasil memodelkan segmentasi pelanggan ritel apotek dengan teknik data mining klasterisasi. Metode yang digunakan adalah melakukan analisis data transaksi pembelian yang terdiri dari atribut Recency Frequency Monetary (RFM) termodifikasi. Analisis telah melibatkan atribut Kuantitas (Quantity) dari data transaksi pembelian produk farmasi obat sebagai eksperimen modifikasi model. Pada proses pemodelan klasterisasi, studi ini menggunakan algoritme data mining K-Means. Hasil penelitian menunjukkan bahwa segmentasi pelanggan yang optimal berada pada dua klaster berdasarkan hasil analisis QRF (Quantity, Recency dan Frequency) menggunakan evaluasi Davies Bouldin Indeks (DBI) dengan nilai 0,527. Kinerja model tersebut dibandingkan dengan algoritme K-Medoids. Hasil klasterisasi pelanggan pada dua kategori menggunakan K-Medoids memiliki nilai DBI sebesar 1.334. Berdasarkan nilai pembanding tersebut maka metode K-Means terbukti lebih baik dalam pembentukan klaster pelanggan ritel farmasi obat pada analisis atribut Quantity, Recency dan Frequency.; AbstractIn general, the purchase of pharmaceutical products in Indonesia has no pattern. The purchase of pharmaceutical products such as medicines, made by individuals not as preparation for maintaining health, but in response to the illness being suffered. On the other hand, retail customers of pharmaceutical drug products are usually influenced by selling price factors and suggestions for certain drug brands when making a purchase. Based on these conditions, the pattern of purchasing drugs for Indonesian people is unpredictable. This makes businesses in the retail business of pharmaceutical drug products, relatively difficult to increase sales value. One effort that businesses can do to increase revenue is to conduct sales promotions based on the type of customer group. Drug pharmaceutical product purchase transactions can be analyzed to determine customer segmentation based on purchase patterns. This research has successfully modeled the pharmacy retail customer segmentation with clustering data mining techniques. The method used is to analyze the purchase transaction data consisting of modified Recency Frequency Monetary (RFM) attributes. Analysis has involved the Quantity attribute (Quantity) of the transaction data of pharmaceutical drug product purchases as a model modification experiment. In the cluster modeling process, this study uses the K-Means data mining algorithm. The results showed that the optimal customer segmentation was in two clusters based on the results of the QRF (Quantity, Recency and Frequency) analysis using the Davies Bouldin Index (DBI) evaluation with a value of 0.527. The performance of the model is compared with the K-Medoids algorithm. The results of customer clustering in two categories using K-Medoids have a DBI value of 1,334. Based on these comparative values, the K-Means method is proven to be better in forming pharmaceutical drug retail customer clusters with analysis Quantity, Recency and Frequency attributes.

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