Proteomics International Laboratories Ltd. · ASX:PIQ
Proteomics International Laboratories — 11th Australian Peptide Conference 2015
26 slides from Proteomics International Laboratories's conference presentation, Oct 26, 2015
Slide 1: ASX Release/Technical Presentation: Translating biomarker discovery into a diagnostic test
Proteomics International Laboratories Ltd ASX Release/Technical Presentation 26 October 2015 For personal use only ASX code: PIQ Translating biomarker discovery into a diagnostic test for diabetic kidney disease Life sciences company Proteomics International Laboratories Ltd (ASX: PIQ) is pleased to
Slide 2: Translating biomarker discovery into a diagnostic test for diabetic kidney disease
Proteomics International For personal use only The Omics Revolution | 25th October 2015 11th Australian Peptide Conference | Kingscliff, New South Wales Translating biomarker discovery into a diagnostic test for diabetic kidney disease Richard Lipscombe 1
Slide 3: Company overview
Proteomics International For personal use only Company: ● Founded 2001 ● Listed on the Australian Stock Exchange April 2015 (Code: PIQ) ● Operates from specialist facilities in Perth, Western Australia People: This accreditation ● Management – ASX-company, biotech trade sales, strengthens the Compan
Slide 4: Outline
Outline For personal use only PromarkerD test Clinical question Process Diagnostic Cross validation Prognostic Predictive Panel 3
Slide 5: The test - PromarkerD
The test - PromarkerD For personal use only Without PromarkerD With PromarkerD 4
Slide 6: Diabetes global burden
Diabetes For personal use only Africa South & Central America Middle East & North Africa North America & Caribbean Europe South-East Asia Western Pacific Total annual cost impact of diabetes in Australia - $14.6 billion 5
Slide 7: Clinical question
Clinical question For personal use only Phenotype: Type 2 diabetes – kidney disease (nephropathy) Glomerular filtration rate (eGFR) Albumin creatinine ratio (ACR) (normo-albuminuria vs. micro- vs. macro) Clinical studies: Fremantle Hospital Diabetes Study (FDS) Longitudinal observational study
Slide 8: Study design
Study design For personal use only Diabetic kidney disease cohorts Discovery Total 3 pools (N = 60) Pool 1 (N=20) Pool 2 (N=20) Pool 3 (N=20) (iTRAQ MS) Normo Micro Macro Pool 1 Pool 2 Pool 3 Mass spectrometry Analytical Total 3 pools (N = 60) (N=20) (N=20) (N=20) Cross validation Normo Micro Macro
Slide 9: Process
Process For personal use only Platform Discovery Validation Clinical Diagnostic Prognostic 2008 2009 2010-11 2012-13 2014-15 8
Slide 10: Targeted MS assay - design
Targeted MS assay - design For personal use only Multiple reaction monitoring assays: Transitions developed for all potential biomarkers High stringency applied to peptide selections to eradicate false signals PeptideAtlas and MRMaid AB Sciex 4000 Q-trap O-labelled reference plasma provide
Slide 11: Targeted MS assay - reproducibility
Targeted MS assay - reproducibility For personal use only Intra- and inter-day peak area profiles (reference plasma) 18 O- versus 13C15N-labelled example: FHR2 peptide LVYPSCEEK 10
Slide 12: Targeted MS assay - reproducibility
Targeted MS assay - reproducibility For personal use only Intra- and inter-day peak area ratios (reference plasma) intra-day CV = 5.9% inter-day CV = 8.1% 11
Slide 13: Analytically validated diagnostic biomarkers
Analytically validated diagnostic biomarkers For personal use only Proteins identified: Inflammation N=3 complement proteins C8, C1q, factor H related p2 Metabolism N=4 adiponectin, apolipoproteins A-IV, B-100, C-III Oxidative stress N=2 peroxiredoxin-2, sulfhydryl oxidase 1 Other N=4 protei
Slide 14: Biomarker cross validation
Biomarker cross validation For personal use only Individual diagnostic biomarker correlations: Plasma protein vs. ACR vs. eGFR concentration Spearman's rho (p < 0.05 highlighted) 13
Slide 15: Biomarker correlations
Biomarker correlations For personal use only P141 N=508 Rho <0.001 14
Slide 16: Study output
For personal use only Study output 15
Slide 17: Correlation to disease - patient stratification
Correlation to disease - patient stratification For personal use only The table shows the distribution of patients when considering both ACR and eGFR measurements and the corresponding risk classification Patient Risk Classification ACR - Normal ACR – Mild ACR – Heavy eGFR 0 --- <3 >3 --- <30 >30 ≥
Slide 18: Diagnostic models
Diagnostic models For personal use only PromarkerD (diagnostic) compared with current commercial biomarker tests Different models define different risk categories (as defined by the ACR or eGFR) PI Biomarker Panel Model Type AUC Specificity Sensitivity PPV NPV DOR ACR>30 Diagnostic 0.75 70% 72%
Slide 19: Correlation to disease - model (high risk)
Correlation to disease - model 1 For personal use only CKD risk=4 High risk patients Those patients with CKD risk=4 as highlighted with any colored boxes below [N=34] ACR - Normal ACR – Mild ACR – Heavy eGFR 0 --- <3 >3 --- <30 >30 ≥ 90 119 59 22 60-89 154 92 23 45-59 31 17 11 30-44 7 18 8 15-29 0 6
Slide 20: Correlation to disease - model (patients at risk)
Correlation to disease - model 2 For personal use only CKD risk≥2 Patients at risk Those patients with CKD risk ≥2 as highlighted with any colored boxes below [N=121] ACR - Normal ACR – Mild ACR – Heavy eGFR 0 --- <3 >3 --- <30 >30 ≥ 90 119 59 22 60-89 154 92 23 45-59 31 17 11 30-44 7 18 8 15-29 0 6
Slide 21: Study design - prognostic
Study design - prognostic For personal use only Diabetic kidney disease cohorts Discovery Total 3 pools (N = 60) Pool 1 (N=20) Pool 2 (N=20) Pool 3 (N=20) (iTRAQ MS) Normo Micro Macro Pool 1 Pool 2 Pool 3 Mass spectrometry Analytical Total 3 pools (N = 60) (N=20) (N=20) (N=20) Cross validation Normo
Slide 22: Prognostic results
Prognostic results For personal use only eGFR decliners – can the biomarkers predict who will develop diabetic kidney disease? This prediction is concerned with the trajectory of the patients’ eGFR - of the current cohort of 349 patients 10% were eGFR decliners A rapidly declining eGFR is one of the
Slide 23: Prognostic model
Prognostic model For personal use only Trajectories – does the panel predict who will decline rapidly? ROC curves for models predicting eGFR decline using 3 biomarkers Clinical predictors: AUC (95% CI) = 0.75 (0.66-0.84) Optimised corrected AUC = 0.73 Clinical predictors + biomarkers: AUC (95%
Slide 24: Summary of results
Summary of results For personal use only The clinical study examined over 500 individuals using two technology platforms; targeted mass spectrometry and antibody systems PromarkerD as a Diagnostic 7 biomarkers were individually validated at high stringency using the mass spectrometry platform, 4 u
Slide 25: PromarkerD - where to next?
PromarkerD - where to next? For personal use only Multiple routes to market: – Specialist diagnostic test run by clinical laboratories (laboratory developed test – LDT) – Standard clinical pathology assay produced by diagnostic companies (in vitro diagnostic – IVD) – Next generation test to monitor
Slide 26: Thank you!
Richard Lipscombe Bill Parker John Dunlop Terry Sweet Andreja Livk Scott Bringans Kaye Winfield Tammy Casey For personal use only Fran Jones Amber Boyatzis Jason Ito Javed Khan Pearl Tan Roop Judge James Lui Kirsten Peters Chuck Morrison Aygu Abzalov Hitormi Lim Christina Andronis Thank you! Tom Kou
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