Model based process optimisation of an industrial chromatographic process for separation of lactoferrin from bovine milk

Journal articleModel based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development. A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute. The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.Lukas Gerstweiler, Paulina Schad, Tatjana Trunzer, Lena Enghauser, Max Mayr, Jagan Billakant

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Primary results from TAIL: A global single-arm safety study of atezolizumab monotherapy in a diverse population of patients with previously treated advanced non-small cell lung cancer

Background Atezolizumab treatment improves survival, with manageable safety, in patients with previously treated advanced/metastatic non-small cell lung cancer. The global phase III/IV study TAIL (NCT03285763) was conducted to evaluate the safety and efficacy of atezolizumab monotherapy in a clinically diverse population of patients with previously treated non-small cell lung cancer, including those not eligible for pivotal trials. Methods Patients with stage IIIB/IV non-small cell lung cancer whose disease progressed after 1-2 lines of chemotherapy were eligible for this open-label, single-arm, multicenter study, including those with severe renal impairment, an Eastern Cooperative Oncology Group performance status of 2, prior anti-programmed death 1 (PD-1) therapy, and autoimmune disease. Atezolizumab was administered intravenously (1200 mg every 3 weeks). Coprimary endpoints were treatment-related serious adverse events and immune-related adverse events. Results 619 patients enrolled and 615 received atezolizumab. At data cutoff, the median follow-up was 12.6 months (95% CI 11.9 to 13.1). Treatment-related serious adverse events occurred in 7.8% and immune-related adverse events in 8.3% of all patients and as follows, respectively, in these subgroups: renal impairment (n=78), 11.5% and 12.8%; Eastern Cooperative Oncology Group performance status of 2 (n=61), 14.8% and 8.2%; prior anti-PD-1 therapy (n=39), 5.1% and 7.7%; and autoimmune disease (n=30), 6.7% and 10.0%. No new safety signals were reported. In the overall population, the median overall survival was 11.1 months (95% CI 8.9 to 12.9), the median progression-free survival was 2.7 months (95% CI 2.1 to 2.8) and the objective response rate was 11%. Conclusions This study confirmed the benefit-risk profile of atezolizumab monotherapy in a clinically diverse population of patients with previously treated non-small cell lung cancer. These safety and efficacy outcomes may inform treatment decisions for patients generally excluded from checkpoint inhibitor trials. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ

Final efficacy and safety data, and exploratory molecular profiling from the phase III ALUR study of alectinib versus chemotherapy in crizotinib-pretreated ALK-positive non-small-cell lung cancer

Background: At the primary data cut-off, the ALUR study demonstrated significantly improved progression-free survival (PFS) and central nervous system (CNS) objective response rate (ORR) with alectinib versus chemotherapy in pretreated, advanced anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer. We report final efficacy and safety data, and exploratory molecular profiling. Patients and methods: Patients who received prior platinum-doublet chemotherapy and crizotinib were randomized 2 : 1 to receive alectinib 600 mg twice daily (n = 79) or chemotherapy (pemetrexed 500 mg/m(2) or docetaxel 75 mg/m(2), every 3 weeks; n = 40) until progressive disease, death or withdrawal. The primary endpoint was investigator-assessed PFS. Secondary endpoints included ORR, CNS ORR and safety. Plasma samples were collected at baseline, then every 6 weeks until progressive disease; molecular factors detected by next-generation sequencing were correlated with outcomes. Results: Investigator-assessed PFS was significantly longer with alectinib than chemotherapy (median 10.9 versus 1.4 months; hazard ratio 0.20, 95% confidence interval 0.12-0.33; P < 0.001). ORR was 50.6% with alectinib versus 2.5% with chemotherapy (P < 0.001). In patients with measurable CNS metastases at baseline, CNS ORR was 66.7% with alectinib versus 0% with chemotherapy (P < 0.001). No new safety signals were seen. ALK rearrangement was identified in 69.5% (n = 41/59) of baseline plasma samples. Confirmed partial responses were observed with alectinib in 6/11 patients with a secondary ALK mutation and 4/6 patients with a non-EML4-ALK (where EML4 is echinoderm microtubule-associated protein-like 4) fusion. Detection of mutant TP53 in baseline plasma resulted in numerically shorter PFS with alectinib (hazard ratio 1.88, 95% confidence interval 0.9-3.93). Conclusions: Final efficacy data from ALUR confirmed the superior PFS, ORR and CNS ORR of alectinib versus chemotherapy in pretreated, advanced ALK-positive non-small-cell lung cancer. Alectinib prolonged PFS versus chemotherapy in patients with wild-type or mutant TP53; however, alectinib activity was considerably decreased in patients with mutant TP53