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Daratumumab is a fully human anti-CD38 IgG1κ monoclonal antibody currently in clinical development for the treatment of multiple myeloma (MM) (PMID:21187443). Novel agents like daratumumab can potentially result in partial or complete clearance of the M-protein or monoclonal immunoglobulin produced by multiple myeloma cells. Monoclonal paraprotein clearance by serum electrophoresis and immunofixation (IFE) is part of the International Multiple Myeloma Working Group (IMWG) criteria for complete response and followed routinely in clinical practice to assess treatment response. The current limit of detection of many serum IFE assays is approximately 150 μg/ml M-protein, which is below the serum concentration of most monoclonal antibodies dosed in the therapeutic range. Indeed, recent studies demonstrated that therapeutic monoclonal antibodies in clinical development for treatment of MM are readily detected on serum IFE and can interfere with the detection and monitoring of endogenous M-protein (PMID: 20940329, 21521182). Therefore, mitigation strategies will need to be put in place to remove this interference to ensure valid clinical response calls that meet IMWG criteria for complete response and stringent complete response (negative serum IFE). We first confirmed daratumumab's interference on serum IFE by spiking various concentrations of antibody into PBS and normal healthy serum. We were able to detect daratumumab in PBS and serum at 50 μg/ml. We then utilized commercially-sourced MM serum samples to determine the M-protein profiles that may be observed in patients undergoing treatment with daratumumab and project the percentage of subjects that may demonstrate interference by serum IFE. Daratumumab was spiked into MM sera at clinically relevant doses (200 μg/ml) and the interference was noted in approximately 50% of IgG kappa samples where daratumumab and endogenous M-protein co-migrated. In remaining IgG kappa samples, the endogenous M-protein migrated in a different position than daratumumab and was easily identifiable. To mitigate interference, we utilized a mouse anti-daratumumab antibody to bind daratumumab and shift the complex migration on IFE. In addition, labeling the anti-idiotype antibody with either biotin or Alexa-fluor tags provided further shift of the daratumumab-complex allowing for additional distinction between the therapeutic antibody and endogenous M-protein. Patients with suspected daratumumab interference can be monitored with this additional reflex serum IFE utilizing the mitigation strategy described above. After clinically validating this assay, we intend to incorporate this mitigation strategy in clinical trials with daratumumab to ensure accurate assessment of clinical M-protein response, a critical evaluation for clinical trial endpoints and disease assessment. Citation Format: Amy E. Axel, Christopher R. McCudden, Hong Xie, Brett M. Hall, A. Kate Sasser. Development of clinical assay to mitigate daratumumab, an IgG1κ monoclonal antibody, interference with serum immunofixation (IFE) and clinical assessment of M-protein response in multiple myeloma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2563. doi:10.1158/1538-7445.AM2014-2563

Background Artificial intelligence (AI) is rapidly being developed and implemented to augment and automate decision-making across healthcare systems. Being an essential part of these systems, laboratories will see significant growth in AI applications for the foreseeable future. Content In laboratory medicine, AI can be used for operational decision-making and automating or augmenting human-based workflows. Specific applications include instrument automation, error detection, forecasting, result interpretation, test utilization, genomics, and image analysis. If not doing so today, clinical laboratories will be using AI routinely in the future, therefore, laboratory experts should understand their potential role in this new area and the opportunities for AI technologies. The roles of laboratorians range from passive provision of data to fuel algorithms to developing entirely new algorithms, with subject matter expertise as a perfect fit in the middle. The technical development of algorithms is only a part of the overall picture, where the type, availability, and quality of data are at least as important. Implementation of AI algorithms also offers technical and usability challenges that need to be understood to be successful. Finally, as AI algorithms continue to become available, it is important to understand how to evaluate their validity and utility in the real world. Summary This review provides an overview of what AI is, examples of how it is currently being used in laboratory medicine, different ways for laboratorians to get involved in algorithm development, and key considerations for AI algorithm implementation and critical evaluation.

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