The Pharmaceutical Excipients Database Hosted on PharmaHUB 2014 GPhA Fall Technical Conference October 27-29, 2014 North Bethesda, MD, MD

Transcription

1 The Pharmaceutical Excipients Database Hosted on PharmaHUB 2014 GPhA Fall Technical Conference October 27-29, 2014 North Bethesda, MD, MD Stephen W. Hoag, Ph.D. University of Maryland, Baltimore School of Pharmacy Phone

2 Disclaimer This presentation contains a summary of the opinion and perspective from GPhA member industry representatives on the topic of The Pharmaceutical Excipients Database Hosted on PharmaHUB. This presentation does not necessarily represent the opinion of the presenter nor its employers.

3 Outline Introduce NIPTE/FDA database project Identify Critical Material Attributes Cataloging Excipient Variability Database structure and usage PharmaHub Database views Applications Future of excipient database Form for collaboration

4 Dosage Form Variability Spec. range Excipients Manufacturing Dosage Forms API Processing Conditions + + +

5 Excipient Properties Excipients are: Highly variable materials Have very different properties and performance attributes as compared to drug substances For example Consider an excipient NF monographs vs API USP monograph To understand excipients, you need to think like an excipient Key issues Identifying Critical Material Attributes (CMA) Most unit operations only empirically understood Understand or identify excipient property variability If develop a product that is on the edge of failure Excipient variability unexpected failure post approval Database can help to solve these key issues

6 Example Ciprofloxacin in USP» Ciprofloxacin Hydrochloride contains not less than 98.0 percent and not more than percent of C 17 H 18 FN 3 O 3 HCl, calculated on the anhydrous basis. Identification A: Infrared Absorption 197K. B: Thin-layer chromatographic plate. C: A solution of it responds to the tests for Chloride 191. Chromatographic purity Calculate the percentage of each impurity peak in the chromatogram obtained from the Assay preparation taken by the formula: 100(r i / r t ) in which r i is the response of each impurity peak; and r t is the sum of the responses of all the peaks: not more than 0.2% of ciprofloxacin ethylenediamine analog or of any other individual impurity peak is found; and the sum of all the impurity peaks is not more than 0.5%. Figure Source USP32-NF27

7 Excipients in NF Monographs» Anhydrous Lactose is: O- -D-galactopyranosyl-(1 4)- -D-glucopyranose ( - lactose) or a mixture of O- -d-galactopyranosyl-(1 4)- -d-glucopyranose and O- -D-galactopyranosyl-(1 4)- -D-glucopyranose ( -lactose). i.e., - lactose or a mixture of - lactose and -lactose Labeling Where the labeling indicates the relative quantities of alpha and beta lactose» Lactose Monohydrate is: a natural disaccharide, obtained from milk, which consists of one glucose and one galactose moiety. [note Lactose Monohydrate may be modified as to its physical characteristics. It may contain varying proportions of amorphous lactose.] Labeling Where the labeling states the particle size distribution, it also indicates the d10, d50, and d90 values and the range for each. For modified Lactose Monohydrate, also label it to indicate the method of modification. Figure Source USP32-NF27

8 Which Raw Material Inputs CQA Excipient Properties Crystal form Particle size Bulk density Etc. Relationships Not well understood Critical Quality Attributes Hardness Disintegration time Dissolution CU/mixing Stability

9 Data Mining Def: Discovery of previously unknown information and patterns from set of data Raw Data Data Mining Hidden Info & Patterns Stage 1 Define problem or subject of study Raw Data Stage 2 Select data Visualize Data Remove noise, & Irrelevant Data Stage 3 Data preprocessing Data Mining Analysis of Results Assimilation of Knowledge Stage 4 Data Analysis

10 Sources of Excipient Variability Intentional (designed into excipient) i.e., different grades and vendors Manufactures produce to improve performance Many examples MCC, HPMC, lactose, et al. Important to understand for: Random Optimizing a formulation performance, in vitro and during production Make regulatory decisions, e.g., excipient substitution and change control Lot-to-lot variation Introduced from all factors disused previously Within a lot variation (container-to-container) During production process parameters can drift Especially continuous process where a lot maybe several days of production Important to understand: so can produce robust formulations

11 Intentional Variation Grades of MCC Particle size 180 um 150 um PH-200 PH-102 SCG 100 um PH-112 PH-102 PH-302 Loose bulk density 50 um PH-113 PH-103 PH-101 PH um NMT 1.5% NMT 2% NMT 3% PH-105 NMT 5% 0.4g/cc FMC NMT= not more than Decrease moisture content 0.3g/cc

12 Intentional Variability Manufacturers of MCC FMC = FMC BIOPOLYMERS JRS = J Rettenmaier & Söhne GmbH and Co.KG AKC = Asahi Kasei Corporation Manufactures Grades Particle Size, µm Moisture, % Loose Bulk Density, g/cc FMC Avicel PH JRS AKC Vivapur Emcocel 50M PH UF KG KG FMC Avicel PH JRS Vivapur Emcocel 90M AKC PH

13 Tracking of Random Variability Track random variability overtime and plot in histogram

14 Where to Host the Database? pharmahub: a cyber infrastructure developed at Purdue University to support digital scholarship, dissemination, collaboration and outreach for the pharmaceutical engineering community. Excipients Database

15 Analysis Tools make decisions and solve problems Derivations & Plots of Measurements histograms, yield loci, powder flow function, frequency & cumulative distributions Measurements raw data, chemical and test descriptions Web Interface Catalogs excipients, vendors, products, lots, test methods, equipment, properties

16 Database Structure Excipients - MCC Products - PH101 USP compendial category Actual product on the market Lots - Lot # 1 Properties - particle size Equipment - Malvern Test method - Light scattering

17 Excipients Database A Publicly Available Database

18 Excipients Database A Publicly Available Database

19 Excipients Database A Publicly Available Database

20 Excipients Database A Publicly Available Database

21 log (MPa) The Mechanical Property Group Particle Size Distribution True Density Bulk Density Cohesiveness Elastic Modulus Compactibility Brittleness D P Slip Region h x Nip Region = 40 D = 8 inches 45 s/d = K = 10 P0 = 0.1 psi h = 45 0 = 0.28 psi w = 4 inches = 11.5 Calculated Johanson s Calculated Schönert = ( ) Effective Yield Locus K log A.Hlinak, NIPTE-NIST Meeting, April,

22 Principal Component 5 Development Lots Based upon random selection of lots: Domain of prior Experience: typical production lots 3 lots used for development can tell if develop formulation using typical material Domain of Prior Experience 7143C 7545C 7513C Source: Kindly supplied by Joe Kushner Principal Component 3 SIMCA-P 11-4/14/ :17:54 AM

23 Inactive Ingredient Database Provide little ability to interact with data

24 Supplier s Data Targeted Experiments to Support Database Literature Data Data Mining Relational Modeling Database User Friendly Web based Interface Database Management Tools User s Data

25 Acknowledgements FDA & NIPTE for funding Ting Wang Ann Christine Catlin Sumudinie Fernando Sudheera R. Fernando Carl Wassgren Kristine Alston Linas Mockus Paribir Basu Individuals with invaluable advice FMC, Brian Carlin Pfizer, Bruno Hancock Roquette, Leon Zhou Colorcon Dave Schoneker IPEC And many others