MIT CAACB Risk Assessment Case Study: Assessing Virus Cross-Contamination Risk between Two Simultaneous Processes in an Open Biomanufacturing Facility.

Some members of MIT's Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB) previously published content on the "Quality Risk Management in the Context of Viral Contamination", which described tools, procedures, and methodologies for assessing and managing the risk of a potential virus contamination in cell culture processes. To address the growing industry interest in moving manufacturing toward open ballrooms with functionally closed systems and to demonstrate how the ideas of risk management can be leveraged to perform a risk assessment, CAACB conducted a case study exercise of these new manufacturing modalities. In the case study exercise, a cross-functional team composed of personnel from many of CAACB's industry membership collaboratively assessed the risks of viral cross-contamination between a human and non-human host cell system in an open manufacturing facility. This open manufacturing facility had no walls to provide architectural separation of two processes occurring simultaneously, specifically a recombinant protein perfusion cell culture process using the human cell line, HEK-293 (Process 1) and a downstream postviral filtration unit operation (Process 2) of a recombinant protein produced in CHO cells. This viral risk assessment focused on cross-contamination of the Process 2 filtration unit operation after the Process 1 perfusion bioreactor was contaminated with a virus that went undetected. The workflow for quality risk management that is recommended by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) was followed, which included identifying and mapping the manufacturing process, defining the risk question, risk evaluation, and risk control. The case study includes a completed Failure Mode and Effects Analysis (FMEA) to provide descriptions of the specific risks and corresponding recommended risk reduction actions.

Patient-Centered Approach to Benefit-Risk Characterization Using Number Needed to Benefit and Number Needed to Harm: Advanced Non-Small-Cell Lung Cancer.

This work summarizes the benefit and risk of the results of clinical trials submitted to the US Food and Drug Administration of therapies for the treatment of non-small cell lung cancer (NSCLC) using number needed to benefit (NNB) and number needed to harm (NNH) metrics. NNB and NNH metrics have been reported as potentially being more patient centric and more intuitive to medical practitioners than more common metrics, such as the hazard ratio, and valuable to medical practitioners in complementing other metrics, such as the median time to event. This approach involved the characterization of efficacy and safety results in terms of NNB and NNH of 30 clinical trials in advanced NSCLC supporting US Food and Drug Administration approval decisions from 2003 to 2017. We assessed trends of NNB over time of treatment (eg, for programmed death 1 inhibitors) and variation of NNB across subpopulations (eg, characterized by epidermal growth factor receptor mutation, programmed death ligand 1 expression, Eastern Cooperative Oncology Group performance status, age, and extent of disease progression). Furthermore, the evolution of NNB of treatments for advanced NSCLC was charted from 2003 to 2017. Across subpopulations, NNB, on average, was 4 patients for approved targeted therapies in molecularly enriched populations, 11 patients for approved therapies in nonmolecularly enriched populations, and 23 patients for withdrawn or unapproved therapies. Furthermore, the NNB analysis showed variation for attributes of epidermal growth factor receptor mutations, level of programmed death 1 expression, Eastern Cooperative Oncology Group performance status, etc. When considering the best-case subpopulations and available drugs, the NNB frontier reduced from an estimated value of 7.7 in 2003 to an estimated value of 2.5 in 2017 at the estimated median overall survival-equal to 6 months-of an untreated patient.

Using a Benefit-Risk Analysis Approach to Capture Regulatory Decision Making: Renal Cell Carcinoma.

Drug regulators such as the US Food and Drug Administration (FDA) make decisions about drug approvals based on benefit-risk analysis. In this work, a quantitative benefit-risk analysis approach captures regulatory decision making about new drugs to treat renal cell carcinoma (RCC). Fifteen FDA decisions on RCC drugs based on clinical trials whose results were published from 2005 to 2018 were identified and analyzed. The benefits and risks of the new drug in each clinical trial were quantified relative to comparators (typically the control arm of the same clinical trial) to estimate whether the benefit-risk was positive or negative. A sensitivity analysis was demonstrated using pazopanib to explore the magnitude of uncertainty. FDA approval decision outcomes for the clinical trials assessed were consistent and logical using this benefit-risk framework.

Using a Benefit-Risk Analysis Approach to Capture Regulatory Decision Making: Melanoma.

Drug regulators seek to make decisions regarding drug approvals based on analysis of the relevant benefits and risks. In this work, 25 US Food and Drug Administration (FDA) decisions on melanoma drugs were identified and analyzed based on clinical trial results published between 1999 and 2017. In each case, the benefits and risks of the new drug in each clinical trial relative to a comparator (typically the control arm of the same clinical trial) were quantified. The benefits and risks were analyzed using a common scale to allow for direct comparison. A sensitivity analysis was conducted using vemurafenib to explore the magnitude of uncertainty in the quantitative assessments. The associated FDA decision outcomes of the new drugs were consistent with the benefits and risks quantified in this work.

A Benefit-Risk Analysis Approach to Capture Regulatory Decision-Making: Multiple Myeloma.

Drug regulators around the world make decisions about drug approvability based on qualitative benefit-risk analysis. In this work, a quantitative benefit-risk analysis approach captures regulatory decision-making about new drugs to treat multiple myeloma (MM). MM assessments have been based on endpoints such as time to progression (TTP), progression-free survival (PFS), and objective response rate (ORR) which are different than benefit-risk analysis based on overall survival (OS). Twenty-three FDA decisions on MM drugs submitted to FDA between 2003 and 2016 were identified and analyzed. The benefits and risks were quantified relative to comparators (typically the control arm of the clinical trial) to estimate whether the median benefit-risk was positive or negative. A sensitivity analysis was demonstrated using ixazomib to explore the magnitude of uncertainty. FDA approval decision outcomes were consistent and logical using this benefit-risk framework.

Light induced fluorescence for predicting API content in tablets: sampling and error.

The use of a light induced fluorescence (LIF) instrument to estimate the total content of fluorescent active pharmaceutical ingredient in a tablet from surface sampling was demonstrated. Different LIF sampling strategies were compared to a total tablet ultraviolet (UV) absorbance test for each tablet. Testing was completed on tablets with triamterene as the active ingredient and on tablets with caffeine as the active ingredient, each with a range of concentrations. The LIF instrument accurately estimated the active ingredient within 10% of total tablet test greater than 95% of the time. The largest error amongst all of the tablets tested was 13%. The RMSEP between the techniques was in the range of 4.4-7.9%. Theory of the error associated with the surface sampling was developed and found to accurately predict the experimental error. This theory uses one empirically determined parameter: the deviation of estimations at different locations on the tablet surface. As this empirical parameter can be found rapidly, correct use of this prediction of error may reduce the effort required for calibration and validation studies of non-destructive surface measurement techniques, and thereby rapidly determine appropriate analytical techniques for estimating content uniformity in tablets.