Biodesix
(Private) Diagnostics

David Brunel, CEO Heinrich Röder, DPhil, CTO Doug Swan, VP-Commercial Operations Paul Beresford, PhD, VP-Business Development and Strategic Marketing Jeffrey Bojar, VP-Legal and Regulatory Affairs
520 Zang Street, Suite 213
Bloomfield, CO 80021 USA
Tel: (303) 417-0500 Fax: (303) 417-9700
Website: http://www.biodesix.com/

In Vitro Diagnostic Products
Biodesix is enabling personalized medicine by focusing on the unique characteristics of the patient using mass spectrometry-based molecular diagnostics. Biodesix has created a set of analysis algorithms, collectively called ProTS, that allow the reliable and routine use of matrix-assisted, laser desorption ionization (MALDI) mass spectrometry (MS) as a diagnostic tool. ProTS performs pre-processing of raw mass spectra obtained from various biological samples such as blood or tissue, rendering them suitable for comparative analysis. It extracts statistically significant features differentiating spectra from distinct clinical groups of samples, creates a classifier that can assign sample spectra to one of these groups, and allows for validation of the results using independent test sets. In oncology, Biodesix continues its research into early diagnostic tools for verifying lung cancer in high risk populations, diagnosis of cancer in males with elevated PSA, alternatives to existing techniques for identifying patients with HEr2-positive breast cancer, stratifying aggressive malignancies from more benign forms, and stratifying patients for reactions to targeted therapies in lung and other malignancies. The power of ProTS analysis has been demonstrated in the discovery of Biodesix’ first product, VeriStrat, that was commercialized in May 2009. VeriStrat is a clinically validated blood test that helps enable more informed decision-making by physicians for patients with advanced non-small cell lung cancer (nsclc). VeriStrat, is a simple blood test, and is the first mass spectrometry diagnostic that uses multiple markers and is shown to be clinically reproducible. VeriStrat is a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI), time of flight (TOF) mass spectrometry (MS) analysis of pretreatment serum to identify patients with advanced nsclc who are likely or not likely to benefit from therapy with EGFr inhibitors. VeriStrat correlates with survival outcomes and is predictive of objective response and disease control in patients with advanced nsclc treated with EGFr inhibitors. Because the test requires a simple blood draw and results are returned within 72 hours, VeriStrat offers a noninvasive method to help oncologists guide treatment decisions quickly. VeriStrat has been validated in clinical trials involving over 1500 patients and tests are processed in Biodesix’ CLIA accredited laboratory.

In June 2010, the USPTO awarded Biodesix patent # 7,736,905 that provides coverage for VeriStrat. Allowed claims are directed towards the serum-based identification of patients likely to benefit from epidermal growth factor (EGF)-targeted therapy. The patent also covers mass spectrometry processes, algorithms and other important aspects of the company’s core technology, ProTS, and represents the cornerstone of the its growing intellectual property portfolio.

The ability of VeriStrat to identify patients with improved TTP and OS was assessed in a separate data set of 40 patients with advanced non-small cell lung cancer (nsclc) treated with bevacizumab (15 mg/kg) every 21 days plus erlotinib (150 mg/day) in the multicenter (n=4), nonrandomized, open label, phase I/II clinical trial (protocol ID: VICC THO 0206; ID01-604; NCT00043823). Participating sites included Carolinas Medical Center (Charlotte, NC), Vanderbilt University Medical Center (Nashville, TN), University of Utah (Salt Lake City, UT), and M. D. Anderson Cancer Center (Houston, TX). In this clinical trial, PFS was 6.2 months and OS was 12.6 months. Of 40 patients, pretreatment serum was available for 35 patients. Reproducibility and variability were tested in 276 spectra from 35 patient samples. Average number of spectra per patient was 8. Of 35 patients, 26 were classified as having a good outcome and 9 were classified as having a bad outcome. Among the 26 patients with a good outcome, good tumor response (PR + long term SD>/=16 weeks) was noted in 16 patients and poor response (PD + short term SD<16 weeks) in 10 patients. Of 9 patients classified as having a bad outcome, good tumor response was observed in 2 patients and poor tumor response in 7 patients. VeriStrat accurately predicted both OS and PFS when applied in a blinded fashion to this cohort of patients using the log-rank test (p=0.004 and p=0.001, respectively). Therefore, MALDI-TOF MS proteomic analysis of pretreatment serum can accurately classify patients with good or poor OS and PFS after treatment with erlotinib and bevacizumab. VeriStrat can assist in the pretreatment selection of patients with nsclc who will show clinical benefit from treatment with erlotinib and bevacizumab (Salmon JS, etal, ASCO08, Abs. 8008).

In June 2007, positive results were published from a clinical trial (prorocol ID: VICC THO 0206, ID01-604; NCT00043823) that used Biodesix’ VeriStrat to provide additional information to oncologists treating non-small cell lung cancer (nsclc). VeriStrat was able to separate patients into groups with statistically different prognoses when treated with inhibitors to the epidermal growth factor receptor (EGFr) such as gefitinib (Iressa; AstraZeneca) and erlotinib (Tarceva; OSI Pharmaceuticals), in a second line setting. Sera collected at two institutions from patients with nsclc before treatment with gefitinib or erlotinib were analyzed by MALDI MS. An algorithm to predict outcomes after treatment with EGFr TKI was developed from a set of 139 patients from 3 cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in 3 control cohorts. The clinical outcomes of survival and TTP were analyzed. An algorithm based on 8 distinct features was developed based on outcomes after EGFr TKI therapy in training set patients. Classification based on spectra acquired at the two institutions had a concordance of 97.1%. In both validation cohorts, the classifier identified patients with improved outcomes after EGFr TKI treatment. In one cohort, MST of patients in the predicted ‘good’ and ‘poor’ groups was 207 and 92 days, respectively (HR of death in the good versus poor groups=0.50). In the other cohort, MST was 306 versus 107 days (HR=0.41). The classifier did not predict outcomes in patients who were not treated with EGFr TKI. This MALDI MS algorithm was not merely prognostic but could classify patients with nsclc for good or poor outcomes after treatment with EGFr TKI, and may thus assist in the pretreatment selection of appropriate subgroups of patients with nsclc for such treatment (Taguchi F, etal, JNCI 2007;99(11):838-846). Also see NCT00043823 trial record in the Combination Trials module.

Current as of June 27, 2010