Pharmaceutical Quality Control of Complex Botanicals

Transcription

1 Pharmaceutical Quality Control of Complex Botanicals Dr Peter Gibson Technical Director Porton Down Science Park, Salisbury, SP4 0JQ, United Kingdom

2 GW Pharmaceuticals To be the global leader in prescription cannabinoid medicines, through the development of pharmaceutical products which address clear unmet patient needs

3 Single Chemical versus Botanical Single Molecule Single identified active Full characterisation Limited number of related substances Botanical One or more actives May not be identified Wide range of components FID1 A, (CHEMSTOR\ \A417843\APB00021.D) FID1 A, (CHEMSTOR\ \A417843\APB00021.D) count count 4800 Cannabinoids THC Sesqui terpenes Di terpenes Mono terpenes Tri terpenes & Waxes mi m

4 FDA Botanical Guidelines (2004) For Phase III prior to NDA submission: Well characterized Batch to batch consistency Multiple fingerprints Qualitatively and quantitatively comparable

5 Lead Product Sativex Oromucosal Spray Spasticity in multiple sclerosis Pain in advanced cancer Neuropathic pain Approved in 24 countries IND in the US Botanical extracts from specific Cannabis sativa plants THC 27mg/ml CBD 25mg/ml

6 Sativex Production Overview THC BRM Botanical Raw Material (BRM) CBD BRM THC BDS Botanical Drug Substance (BDS) CBD BDS Botanical Drug Product (BDP) Sativex

7 Cannabis Production Weeks 1 to 3

8 Cannabis Production Weeks 4 to 11

9 Cannabis Production Weeks 4 to 11

10 BRM to BDS THC BRM CBD BRM Milling Decarboxylation CO 2 Extraction Partial Purification Isolation THC BDS CBD BDS

11 BDS to Sativex THC BDS CBD BDS Bulk Solution Filling Packaging Sativex Finished Product

12 BDS Characterization Cannabinoids Non cannabinoids 70 to 75% w/w 25 to 30% w/w

13 BDS Total Components Identified compounds present in the BDS n = 73 n = 79

14 Specification Quantitative 10 cannabinoids 14 non cannabinoid components Class totals Qualitative Chromatogram comparison Routine use of 6 methods

15 Qualitative Compare Chromatograms

16 Overview 4 methods Cannabinoids Terpenes Triglycerides Sterols & Triterpenes

17 Basis for Interpretation PC2 PC3 PC1

18 System Approach PCA Model Cannabinoids PCA Model Terpenes PCA Model Sterols PCA Model Triglycerides Compare new batch Compare new batch Compare new batch Compare new batch Pass/Warn/Fail Pass/Warn/Fail Pass/Warn/Fail Pass/Warn/Fail Pass/Warn/Fail Qualitative Fit of Batch

19 Qualitative Assessment Process Fingerprinting Automate batch assessment using pass warn fail system

20 Alignment issue Instrument Column Mobile Phase

21 Raw and Aligned Data Raw Data Aligned

22 Model from Aligned Profiles Each evaluation has two qualifiers in model consistency Mahalanobis distance out of model variation Sample Residual PCA Scores Warning limit (95% confidence interval) Failure limit (99% confidence interval)

23 CBD BDS Terpene Analysis Outliers in bold 23

24 CBD BDS Cannabinoid Analysis Outliers in bold

25 THC BDS Models Cannabinoids Terpene s Sterols Triglyceride s

26 CBD BDS Models Cannabinoids Terpene s Sterols Triglyceride s

27 Sativex Models Cannabinoids Terpenes

28 Model Development PCA Model Cannabinoids PCA Model Terpenes PCA Model Sterols PCA Model Triglycerides Compare new batch Compare new batch Compare new batch Compare new batch Pass/Warn/Fail Pass/Warn/Fail Pass/Warn/Fail Pass/Warn/Fail Pass/Warn/Fail Qualitative Fit of Batch

29 QC Analysis Schema the Profiler THC or CBD or Sativex Cannabinoids (LC) Sterols (GC) Terpenes (GC) Triglycerides (LC) CDF CDF CDF CDF Align Signals CDF CDF CDF CDF Compare Fingerprint to model MD & Q Report Scoresheet

30 Batch Scoring For MD, Q if metric < 95% cutoff, score = 0 if metric > 95% cutoff, score = 1 if metric > 99% cutoff, score = 3 Cannabinoids Fail if (primary) score >= 3 Warn if score > 0 Non cannabinoids Fail if composite (secondary) score >= 9 Warn if composite score >= 3

31 Investigate Warn/Fail CBN THCV CBC

32 Summary Routine QC Procedure Based on strong data set Demonstrates can identify batches Absence of expected constituents Presence of the unexpected peak Variation in the expected relative abundance Chromatographic changes beyond the scope of the training set

33 Thanks to: Infometrics Inc Brian Rohrback Scott Ramos Analytical R&D team at GW Pharma Alan Sutton Emma Lennon James Dean Hughes