Lhasa Limited Collaborative Data & Knowledge Sharing

Transcription

1 Lhasa Limited Collaborative Data & Knowledge Sharing Featuring: Elemental Impurities and PDE/AI Data Sharing Dr Liz Covey-Crump Acknowledgements: Dr Crina Heghes, Dr Will Drewe & Dr Andy Teasdale

2 Presentation Outline Data Sharing Overview Data Sharing Benefits Lhasa Limited Member projects Elemental Impurities Data Sharing PDE/AI 2

3 Data/Knowledge Sharing Projects Overview Data Sharing Aromatic Amines Excipients Production Intermediates Elemental Impurities PDEs Regulatory Authorities Knowledge Sharing Derek Nexus, Sarah Nexus, Zeneth, Meteor Nexus, Mirabilis

4 DATA SHARING BENEFITS

5 Aromatic Amines/Intermediates Mutagenicity Databases Prevent duplication of compound testing Saves time and money, 5 10k EUROs per Ames Screening (Dr Joerg Wichard, Bayer AG) Besides pure data sharing other topics are discussed which can often be diversely managed by different companies Topics include:- In vivo testing as a follow up to mitigate a +ve Ames test Different degrees of success with regulators re. not Ames testing ICH M7 Option 4 compounds (Dr Susanne Glowienke, Novartis) 2 strain vs 5 strain Ames testing (Joerg W) Major improvements to in silico tools for the assessment of mutagenicity (Dr Andrew Teasdale, AZ)

6 Excipients/Vehicles Toxicity Database Dr Joerg Wichard Bayer AG Receives frequent requests for analyses of excipients/vehicles in order to provide up front information regarding tolerability Prevents progressing with an unsuitable vehicle choice Helps provide alternative vehicle suggestions

7 ELEMENTAL IMPURITIES

8 Why Share Data? ICH Q3D is predicated on the evaluation of risk, this is made up of 3 factors RISK = PROBABILITY x Severity x Detectability We know the severity Defined PDEs. We have detectability ICP / XRF DATA either newly generated or historical data informs us as to the probability. Sharing data thus allows us to make an informed judgement during the identify and evaluate phases

9 Why Share Data? Q3D :- Section 5 - Information for this risk assessment includes but is not limited to: data generated by the applicant, information supplied by drug substance and/or excipient manufacturers and/or data available in published literature. Section The data that support this risk assessment can come from a number of sources that include, but are not limited to: Prior knowledge; Published literature; Data generated from similar processes; Supplier information or data; Testing of the components of the drug product; Testing of the drug product.

10 What data already exists? Container closure systems First example of a data sharing initiative Theoretical risk of metals leaching from CCS into liquid formulations (in particular) Data suggested that the probability is minimal and therefore does not require further consideration in the risk assessment Jenke D. et al., A Compilation of Metals and Trace Elements Extracted from Materials Relevant to Pharmaceutical Applications Such as Packaging Systems and Devices, PDA J. Pharm. Sci. Technol., 67(4):354-75,2013 Elemental Impurities in Pharmaceutical Excipients 200 Samples relating to 24 elements Little evidence of substantial levels of even big 4/Class 1 in mined excipients Gang L.L et al., Elemental Impurities in Pharmaceutical Excipients., Pharmaceutics, Drug Delivery and Pharmaceutical Technology., 104: , 2015

11 Data Sharing Share the analytical data generated to establish the levels of trace metals within batches of excipients used in the manufacture of pharmaceuticals. Aim Facilitate more scientifically driven elemental impurities risk assessments and reduce unnecessary testing

12 What s the strategic intent of the database? Become the primary source of EI data for excipients that drives initial risk assessment (c.f. the Jenke paper for packaging components & EIs) Publish key findings with the intention of de-risking commonly used excipients Compare / contrast with data published generated by FDA.

13 Building the database Lhasa Limited designed and developed the Elemental Impurities database based on the Vitic Nexus platform Approved by the consortium in December 2015 Initial round of donations was received beginning of 2016 The database was first released at the end of March 2016 V released December Excipients 757 Result Records

14 Building the database Procedure/process for organizations to share their in-house data Template defined to allow error free parsing of data Data anonymised and checked by Lhasa Limited

15 Building the database Data quality requirements Extensive discussions relating to data requirements Validation protocol generated Extent of Validation recorded + Digestion Conditions No difference between the data donated and data published in a peer reviewed journal Sub Class A Compare a matrix matched blank to your lowest standard, making sure there is no significant contribution compared to your lowest standard Minimum 5 point calibration R = >0.995 ~ >R 2 = Minimum of 2 spikes one at the top and one at the bottom of the quantitative liner range spike recoveries are between % Governed by Accuracy and Range data. 6 replicate aspirations of a standard or spiked sample either together or taken throughout the analysis giving %RSD 20% or spike sample or standard tested at the start and end of the run give the same measurement ± 20% or a 5 point calibration gives an R value of Minimum N=3 replicate spikes within the Range of the method, The spikes can be at the same level or different levels where the response factors give 20% RSD As long as test solutions and spikes are prepared within 24 hours of each other solution stability is assumed as long as all other parameters are met. Equivalent concentration in ug/g in sample of your lowest spike Equivalent concentration in ug/g in sample of your lowest and highest spike Estimate LOD by taking the Std Dev of 6 blank measurements, multiplying by 3.3 and dividing this by the slope of your calibration line. Sub class B Compare a matrix matched blank to your lowest standard, making sure there is no significant contribution compared to your lowest standard Minimum 3 point calibration R = >0.990 ~ >R 2 = Minimum of 1 spike within the quantitative liner range spike recoveries are between % Governed by Accuracy and Range data. 6 replicate aspirations of a standard or spiked sample either together or taken throughout the analysis giving %RSD 20% or sample tested at the start and end of the run give the same measurement ± 30% or a 5 point calibration gives an R value of Minimum of 2 spikes one at the top and one at the bottom of the quantitative liner range spike recoveries are between % As long as test solutions and linearity standards are prepared within 48 hours of each other solution stability is assumed as long as all other parameters are met. Equivalent concentration in ug/g in sample of your lowest standard Equivalent concentration in ug/g in sample of your lowest and highest standard Estimate LOD by taking the Std Dev of 6 blank measurements, multiplying by 3.3 and dividing this by the slope of your calibration line.

16 Building a Database Is all of the data for lactose? How will sufficient diversity of materials and suppliers be managed? ListNo CarlMrozListName Total 1 Magnesium stearate 23 2 Microcrystalline cellulose 41 3 Lactose 32 4 Starch 14 5 Cellulose derivatives 18 6 Sucrose 9 7 Povidone 15 8 Stearic acid 3 9 Dibasic calcium phosphate Polyethylene glycol 6 Number of results Data will be generated if gaps are identified

17 How will the database be used? The database will provide a better assessment of which materials represent a more significant risk than others Indicate where the risk is real and where it is negligible Reduce the amount of testing that is needed The EI database can be used as key supportive information in conjunction with some product specific test data for a risk assessment Various cases studies have been presented in a Lhasa vicgm Please let me or crina.heghes@lhasalimited.org. know if you re interested to join this initiative.

18 ACCEPTABLE INTAKE (AI) AND PERMITTED DAILY EXPOSURE (PDE) DATA SHARING PROJECT FOR PHARMACEUTICAL IMPURITIES

19 Background API Synthesis involves the use of substances which may exert toxic effects and therefore pose a risk to human health if present as an impurity. (Residual) solvents, intermediates or reagents. Extractables and leachables from medical devices etc. Several guidelines regulate the management of these impurities, and include the use of AI or PDEs for managing the allowable limit of exposure to these residual chemicals in drug products Where an AI or PDE cannot be determined, default to the threshold for toxicological concern (TTC; 1.5 µg/person/day) or use other methods to prove absence

20 What is an AI or a PDE monograph? AI Acceptable Intake For mutagenic carcinogens without a threshold mechanism and with known carcinogenicity data. find most sensitive tumour site (conservative) consider route of exposure AI calculated for each route of exposure based on the most sensitive tumour site PDE Permitted Daily Exposure For non-mutagenic carcinogens with a threshold mechanism, a non-linear dose response or a mechanism which has no human relevance. e.g. solvents used in synthesis Use the NOEL or LOEL in the calculation Examples & methodology - ICH M7 addendum document.

21 Share AI/PDE Safety Assessment Data Generate an agreed upon series of AIs/PDEs for common impurities in APIs (reagents/solvents) Share existing in-house data initially- reagents, solvents, impurities. Include the ICH M7 addendum chemicals and others which are/or soon will be publically available. Harmonise the approach to conduct safety assessments and derive an AI/PDE Industry accepted process developed and agreed upon through the ICH M7 addendum exercise. The monograph (pdf/word file) is the vehicle for documenting an AI/PDE. Typically contains only public data. Shared AI/PDE data to be peer reviewed & agreed Aim single agreed AI/PDE value per chemical

22 Share AI/PDE Safety Assessment Data The AI/PDE data will be made available through a database that can be searched by structure, CAS, etc. Access to this resource will save a significant amount of time and cost. Initial database functionality requirements established. Key requirement is access to the supporting monograph document (word/pdf).

23 Project Structure and Technical Solution Members share data PDE monographs (PDF/Word text documents) Extract AI/PDE data to schema Vitic Nexus schema to include: Structure, CAS, name etc. AI/PDE Source/version Hyperlink Release VXD to Members with inhouse Vitic Nexus Lhasa data sharing process Provide online access to Members without in-house Vitic Nexus Data donation deadline Store monograph (PDF) in a private/hidden document library in the Member area of the Lhasa Limited website Lhasa Limited will store and host the data securely & control user access.

24 Short-Listing Chemicals of Greatest Interest Data survey Results 58 unique chemicals suggested for sharing within the project. 74 including those from the ICH M7 addendum Most have a monograph already developed for sharing some do not. However many of these require updating.

25 Next Steps Collectively work towards donating at least 2 AI/PDE monographs for the 31st of May Kick-off the peer-review process for approx. 10 weeks from that point Fifteen organisations have expressed an interest in participating Please let me or will.drewe@lhasalimited.org if you re interested to join this initiative.

26 Conclusions Data and Knowledge sharing have shown benefits for:- Saving time and money Reduction of testing Improving models Standardising methodology/approach across industry Highlighting discrepancies There has a considerable increase in the volume of knowledge/data shared since Lhasa Limited inception in 1983