On April 26, 2016 Zymeworks Inc., a leader in the development of bi-specific and multi-specific antibodies and antibody drug conjugates, reported that it has entered into a new licensing agreement with GSK for the research, development, and commercialization of novel bi-specific antibodies enabled using Zymeworks’ Azymetric drug discovery platform (Press release, Zymeworks, APR 26, 2016, View Source [SID1234536466]). Under the agreement, GSK will have the option to develop and commercialize multiple bi-specific drugs across different disease areas. Zymeworks will receive upfront and preclinical payments of up to USD$36 million and is eligible to receive up to USD$152 million in development and clinical milestone payments, along with commercial sales milestone payments of up to USD$720 million, and tiered royalties on potential sales.

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As previously announced in December 2015, Zymeworks and GSK entered into a collaboration and license agreement to further develop Zymeworks’ Effector Function Enhancement and Control Technology (EFECT) platform and to research, develop, and commercialize novel Fc-engineered monoclonal and bi-specific antibody therapeutics that have been optimized for specific therapeutic effects. As part of this second agreement, GSK has also gained the right to combine the Azymetric platform with novel engineered Fc domains developed under the previously announced collaboration.

"We are excited to be expanding our relationship with GSK to include our Azymetric bi-specific platform. We view this new collaboration as evidence of our valuable role as a partner and the strength of our proprietary drug development platforms," said Ali Tehrani, Ph.D., President and CEO of Zymeworks. "The proceeds from this collaboration will be used to advance our pipeline of therapeutic candidates, including the Azymetric antibody ZW25 and the Azymetric antibody drug conjugate ZW33, into human clinical trials this year. They will also be utilized to support the continued expansion and strengthening of our core capabilities in antibody discovery, protein engineering, and antibody drug conjugates."

About the Azymetric Platform
Bi-specific antibodies developed using the Azymetric platform resemble conventional mono-specific antibodies while being able to simultaneously bind to two different targets resulting in additive or synergistic therapeutic responses. Azymetric antibodies spontaneously assemble into a single molecule with two different Fab domains comprising of unique heavy and light chain pairings. Azymetric antibodies are manufactured using conventional monoclonal antibody processes and can also be easily adapted to rapidly screen target and sequence combinations for bi-specific activities in the final therapeutic format thereby significantly reducing drug development timelines.

About the EFECT Platform
The EFECT platform is a library of antibody Fc modifications engineered to modulate the activity of the antibody-mediated immune response, which includes both the up and down-regulation of effector functions. This platform is compatible with traditional monoclonal as well as Azymetric bi-specific antibodies to further enable the customization of therapeutic responses for different diseases.

To prospectively test the diagnostic accuracy of the percentage of prostate specific antigen (PSA) isoform [-2]proPSA (%p2PSA) and the Prostate Health Index (PHI), and to determine their role for discrimination between significant and insignificant prostate cancer at initial and repeat prostate biopsy in men aged ≤65 years.
The diagnostic performance of %p2PSA and PHI were evaluated in a multicentre study. In all, 769 men aged ≤65 years scheduled for initial or repeat prostate biopsy were recruited in four sites based on a total PSA (t-PSA) level of 1.6-8.0 ng/mL World Health Organization (WHO) calibrated (2-10 ng/mL Hybritech-calibrated). Serum samples were measured for the concentration of t-PSA, free PSA (f-PSA) and p2PSA with Beckman Coulter immunoassays on Access-2 or DxI800 instruments. PHI was calculated as (p2PSA/f-PSA × √t-PSA). Uni- and multivariable logistic regression models and an artificial neural network (ANN) were complemented by decision curve analysis (DCA).
In univariate analysis %p2PSA and PHI were the best predictors of prostate cancer detection in all patients (area under the curve [AUC] 0.72 and 0.73, respectively), at initial (AUC 0.67 and 0.69) and repeat biopsy (AUC 0.74 and 0.74). t-PSA and %f-PSA performed less accurately for all patients (AUC 0.54 and 0.62). For detection of significant prostate cancer (based on Prostate Cancer Research International Active Surveillance [PRIAS] criteria) the %p2PSA and PHI equally demonstrated best performance (AUC 0.70 and 0.73) compared with t-PSA and %f-PSA (AUC 0.54 and 0.59). In multivariate analysis PHI we added to a base model of age, prostate volume, digital rectal examination, t-PSA and %f-PSA. PHI was strongest in predicting prostate cancer in all patients, at initial and repeat biopsy and for significant prostate cancer (AUC 0.73, 0.68, 0.78 and 0.72, respectively). In DCA for all patients the ANN showed the broadest threshold probability and best net benefit. PHI as single parameter and the base model + PHI were equivalent with threshold probability and net benefit nearing those of the ANN. For significant cancers the ANN was the strongest parameter in DCA.
The present multicentre study showed that %p2PSA and PHI have a superior diagnostic performance for detecting prostate cancer in the PSA range of 1.6-8.0 ng/mL compared with t-PSA and %f-PSA at initial and repeat biopsy and for predicting significant prostate cancer in men aged ≤65 years. They are equally superior for counselling patients before biopsy.
© 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.

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The treatment of autoimmune disorders has been revolutionised by the introduction of biologics such as anti-tumour necrosis factor (anti-TNF). Although in rheumatoid arthritis patients a bone sparing effect of anti-TNF has been shown, the mechanism is not fully understood. Anti-TNF molecules block tumour necrosis factor (TNF) and prevent signalling via both TNF receptor 1 (TNFR1; p55) and TNF receptor 2 (TNFR2; p75). However, signalling via TNFR2 is reported to have protective effects in a number of cell and organ systems. Hence we set out to investigate if pharmacological inhibition of TNFR1 had differential effects compared to pan-TNF inhibition in both an in vitro cell-based model of human osteoclast activity and an in vivo mouse model of lipopolysaccharide (LPS)-induced osteolysis. For the in vitro experiments the anti-human TNFR1 domain antibody (dAb) DMS5541 was used, whereas for the in vivo mouse experiments the anti-mouse TNFR1 dAb DMS5540 was used. We show that selective blocking of TNFR1 signalling reduced osteoclast formation in the presence of TNF. Subcutaneous LPS injection over the calvaria leads to the development of osteolytic lesions within days due to inflammation driven osteoclast formation. In this model, murine TNFR2 genetically fused with mouse IgG1 Fc domain (mTNFR2.Fc), an anti-TNF, did not protect from bone loss in contrast to anti-TNFR1, which significantly reduced lesion development, inflammatory infiltrate, and osteoclast number and size. These results support further exploring the use of TNFR1-selective inhibition in inflammatory bone loss disorders such as osteomyelitis and peri-prosthetic aseptic loosening.
Copyright © 2015 Elsevier Inc. All rights reserved.

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Anti-tumor cellular immunotherapies that implement a suicide gene system can limit potential undesirable effects. In a haplo-identical bone marrow transplant clinical trial, over 90% of iCaspase-9-expressing cells were eradicated after AP1903 exposure, and signs of graft-versus-host disease disappeared. Nevertheless, low numbers of genetically-modified T cells survived this treatment. We studied genetically-modified cell lines (GMCL) that carried a dual iCaspase-9/ΔCD19 DNA construct (ΔCD19=truncated CD19). With AP1903 exposure, a low percentage of cells (1.47%±0.67%; n=5 replications) persisted in-vitro. Repeated exposures to increasing AP1903 doses generated low (GMCLLR) and high AP1903-responders (GMCLHR), which expressed different levels of surface ΔCD19and intracellular iCaspase-9. Compared to GMCLHR, GMCLLR exhibited higher methylation of 5′-long-terminal repeat (LTR) promoters, both in the number of sequences with at least one methylated CpG (16 vs 51.5%, respectively) and in the number of CpG islands (1.2 vs 8.9%, respectively). Four days of 5-azacytidine exposure reduced methylation and increased ΔCD19 and iCaspase-9 expression. Interestingly, LTR demethylation restored GMCLLR sensitivity to AP1903 by 24.3-fold (1.8 vs 43.8%) without affecting GMCLHR. We showed that 5’LTR-methylation inhibited transgene expression and caused AP1903 hypo-responsiveness. Treating with a hypomethylating agent restored AP1903 sensitivity. This approach can be applied in further clinical trials to improve iCaspase-9 response if low response is detected.Gene Therapy accepted article preview online, 25 April 2016. doi:10.1038/gt.2016.39.

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Patients with previously untreated BRAF V600E mutation-positive melanoma in BREAK-3 showed a median overall survival (OS) of 18.2 months for dabrafenib versus 15.6 months for dacarbazine (hazard ratio [HR], 0.76; 95% confidence interval, 0.48-1.21). Because patients receiving dacarbazine were allowed to switch to dabrafenib at disease progression, we attempted to adjust for the confounding effects on OS.
Rank preserving structural failure time models (RPSFTMs) and the iterative parameter estimation (IPE) algorithm were used. Two analyses, "treatment group" (assumes treatment effect could continue until death) and "on-treatment observed" (assumes treatment effect disappears with discontinuation), were used to test the assumptions around the durability of the treatment effect.
A total of 36 of 63 patients (57%) receiving dacarbazine switched to dabrafenib. The adjusted OS HRs ranged from 0.50 to 0.55, depending on the analysis. The RPSFTM and IPE "treatment group" and "on-treatment observed" analyses performed similarly well.
RPSFTM and IPE analyses resulted in point estimates for the OS HR that indicate a substantial increase in the treatment effect compared with the unadjusted OS HR of 0.76. The results are uncertain because of the assumptions associated with the adjustment methods. The confidence intervals continued to cross 1.00; thus, the adjusted estimates did not provide statistically significant evidence of a treatment benefit on survival. However, it is clear that a standard intention-to-treat analysis will be confounded in the presence of treatment switching-a reliance on unadjusted analyses could lead to inappropriate practice. Adjustment analyses provide useful additional information on the estimated treatment effects to inform decision making.
Treatment switching is common in oncology trials, and the implications of this for the interpretation of the clinical effectiveness and cost-effectiveness of the novel treatment are important to consider. If patients who switch treatments benefit from the experimental treatment and a standard intention-to-treat analysis is conducted, the overall survival advantage associated with the new treatment could be underestimated. The present study applied established statistical methods to adjust for treatment switching in a trial that compared dabrafenib and dacarbazine for metastatic melanoma. The results showed that this led to a substantially increased estimate of the overall survival treatment effect associated with dabrafenib.
©AlphaMed Press.

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