Malaria Slide Bank to Strengthen and Improve the Quality of Malaria Diagnosis: A National Slide Repository in India

Malaria elimination is one of the top health care priorities in India, necessitating accessible and accurate diagnosis for effective treatment. A malaria slide bank in India is a collection of quality-controlled malaria-positive and -negative slides and is considered a vital asset for quality diagnosis. The collection of blood samples, preparation of blood smears, staining, quality control, molecular characterizations, and slide validation were carried out according to standard operating procedures in accordance with the WHO reference laboratory. The true count and parasite density per microliter were computed in accordance with WHO guidelines. Over 27 months, 48 batches (8,196 slides) were prepared. Overall, the majority of slide batches were Plasmodium vivax (45.9%; 22/48), followed by Plasmodium falciparum (25%; 12/48), malaria-negative infections (25%; 12/48), and mixed infections (4.1%; 2/48). All 48 batches passed internal validation by WHO-certified level-1 microscopists. For a batch, the true count was the median of the validators' counts (range, 111-280,795 parasites/μL). Except for mixed infections, the PCR results agreed with the verified microscopy results. Malaria slide bank slides would be a valuable tool for quality control, assurance, and microscopist training.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

BACKGROUND

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants.

OBJECTIVE

We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques.

METHOD

Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species.

RESULT

A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration.

CONCLUSION

We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Monoclonal antibody: future of malaria control and prevention

Monoclonal antibodies (mAbs) are extremely specialized proteins that are cloned from B cells and bind to pathogen epitopes. There are currently no known prophylactic immune-based strategies or efficient, widespread treatments to stop the spread of malaria. In order to lower the prevalence of malaria and its associated mortality, we need mAbs that are capable of offering immediate passive protection against the disease. mAbs have become more crucial in the treatment or prevention of several other infectious diseases. Recently, mAb development for malaria prevention and control has greatly evolved and widespread use in public health settings is now a possibility.

Continuous manufacturing of monoclonal antibodies: Dynamic control of multiple integrated polishing chromatography steps using BioSMB

We propose a strategy for automation and control of multi-step polishing chromatography in integrated continuous manufacturing of monoclonal antibodies. The strategy is demonstrated for a multi-step polishing process consisting of cation exchange chromatography in bind-and-elute mode followed by mixed-mode chromatography in flowthrough mode. A BioSMB system with a customized Python control layer is used for automation and scheduling of both the chromatography steps. Further, the BioSMB valve manifold is leveraged for in-line conditioning between the two steps, as tight control of pH and conductivity is essential when operating with multimodal resins because even slight fluctuations in load conditions adversely affect the chromatography performance. The pH and conductivity of the load to the multimodal chromatography columns is consistent, despite the elution gradient of the preceding cation exchange chromatography step. Inputs from the BioSMB pH and conductivity sensors are used for real-time control of the 7 pumps and 240 valves to achieve in-line conditioning inside the BioSMB manifold in a fully automated manner. This is confirmed by showcasing different elution strategies in cation exchange chromatography, including linear gradient, step gradient and process deviations like tubing leakage. In all the above cases, the model was able to maintain the pH and conductivity of multimodal chromatography load within the range of 6 ± 0.1 pH and 7 ± 0.3 mS/cm conductivity. The strategy eliminates the need for using multiple BioSMB units or integrating external pumps, valves, mixers, surge tanks, or sensors between the two steps as is currently the standard approach, thus offering a simple and robust structure for integrating multiple polishing chromatography steps in continuous downstream monoclonal antibody purification trains.

Post-refolding stability considerations for optimization of in-vitro refolding: L-asparaginase as a case study

BACKGROUND

L-Asparaginase is an essential enzyme for the food and biopharmaceutical industry. The stability, however, of L-asparaginase is widely known to be an issue. Commercial manufacturing of any biopharmaceutical involves hold-ups during processing, and can result in product loss if stability is an issue, as is the case with L-asparaginase. This interplay of product intermediate stability and process design is the focus of this investigation.

METHODS AND RESULTS

In this study, we propose a strategy to simultaneously increase the refolding yield and stability of refolded L-asparaginase so as to improve overall process yield. Using one variable at a time (OVAT) experiments, urea (6 M), solubilized inclusion bodies (15 mg/ml), refolding method (step dilution), and pH (8.6) were identified as significant process parameters. A design of experiment (DOE)-based optimization was then performed for the refolding step. The net outcome was more than a three-fold increase in enzyme recovery (i.e., 4.90 IU/ml) compared to unoptimized conditions (i.e., 1.26 IU/ml). Further, the L-asparaginase process intermediate was found to be stable for more than a week at room temperature and 2-8°C, while the unoptimized sample was stable at 2-8°C but did not show any activity at room temperature after 72 h.

CONCLUSIONS

The current study elucidates how process intermediate stability needs to be given due consideration during process optimization, particularly for products such as L-asparaginase which are labile.

Linear Growth Trajectories, Catch-up Growth, and Its Predictors Among North Indian Small-for-Gestational Age Low Birthweight Infants: A Secondary Data Analysis

Background

Low birthweight small-for-gestational-age (SGA-LBW) (birthweight below the 10th percentile for gestational age; SGA-LBW) infants are at an increased risk of poor postnatal growth outcomes. Linear growth trajectories of SGA-LBW infants are less studied in South Asian settings including India.

Objectives

To describe the linear growth trajectories of the SGA-LBW infants compared with appropriate-for-gestational-age LBW (AGA-LBW) infants during the first 6 months of life. In addition, we estimated catch-up growth (ΔLAZ > 0.67) in SGA-LBW infants and their performance against the WHO linear growth velocity cut-offs. Additionally, we studied factors associated with poor catch-up growth in SGA-LBW infants.

Methods

The data utilized came from an individually randomized controlled trial that included low birthweight (LBW) infants weighing 1,500–2,250 g at birth. A total of 8,360 LBW infants were included. For comparison between SGA-LBW and AGA-LBW infants, we presented unadjusted and adjusted estimates for mean differences (MDs) or risk ratios (RRs) for the outcomes of length, linear growth velocity, length for age z-score (LAZ) score, and stunting. We estimated the proportion of catch-up growth. Generalized linear models of the Poisson family with log links were used to identify factors associated with poor catch-up growth in SGA-LBW infants.

Results

Low birthweight small-for-gestational-age infants had a higher risk of stunting, lower attained length, and a lower LAZ score throughout the first 6 months of life compared with AGA-LBW infants, with differences being maximum at 28 days and minimum at 6 months of age. The linear growth velocity in SGA-LBW infants compared with AGA-LBW infants was significantly lower during the birth–28 day period [MD −0.19, 95% confidence interval (CI): −0.28 to −0.10] and higher during the 3- to 6-month period (MD 0.17, 95% CI: 0.06–0.28). Among the SGA-LBW infants, 55% showed catch-up growth for length at 6 months of age. Lower wealth quintiles, high birth order, home birth, male child, term delivery, non-exclusive breastfeeding, and pneumonia were associated with the higher risk of poor catch-up in linear growth among SGA-LBW infants.

Conclusion

Small for gestational age (SGA) status at birth, independent of gestational age, is a determinant of poor postnatal linear growth. Promotion of institutional deliveries, exclusive breastfeeding, and prevention and early treatment of pneumonia may be helpful to improve linear growth in SGA-LBW infants during early infancy.

Clinical Trial Registration

[https://clinicaltrials.gov/], identifier [NCT02653534].

Discriminatory ability of mid-upper arm circumference in identifying overweight and obese adolescents: Findings from the comprehensive national nutrition survey, India

Background

Overweight and obesity during adolescence is an important public health problem. However, little is known about the age-and sex-specific mid-upper arm circumference (MUAC) cut-offs for identifying overweight and obese adolescents.

Objectives

The present study was planned to assess diagnostic performance of MUAC in identifying overweight and obese adolescents and estimating age specific MUAC cut-offs, separately for males and females, taking body mass index for age Z-score (BAZ) as the gold standard.

Methods

The present study is secondary data analysis using Comprehensive National Nutrition Survey, India, on 31,471 adolescents. The, area under curve receiver operating characteristic curve (AUC), and Youden Index were used to estimate MUAC cut-offs for overweight (BAZ > +1) and obesity (BAZ > +2).

Results

The MUAC cut-offs to identify overweight were: For 10-14 years- 22.9/23.4 cm, for 15-19 years - 27.0/25.6 cm for males and females, respectively; and for obesity were: For 10-14 years - 24.5/25.1, for 15-19 years - 28.5/28.0 cm for males and females, respectively. For overweight, among males, the age-specific cut-off ranged between 21.2 cm (10 years) and 29.8 (19 years), and for females ranged between 21.2 cm (10 years) and 26.7 cm (19 years). For obesity, it ranged between 22.4 cm (10 years) and 31.1 cm (18 years) for males, and 23.9 cm (10 years) to 26.9 cm (19 years) for females. For obesity, AUC ranged between 0.81 and 0.92, indicating good to excellent diagnostic accuracy.

Conclusion

Age- and sex-specific MUAC cut-offs could be considered as a screening tool for identifying overweight and obese adolescents.

Raman spectroscopy for in situ, real time monitoring of protein aggregation in lyophilized biotherapeutic products

Quality of biotherapeutic products is of paramount importance for ensuring patient safety. Analytical tools that can facilitate rapid quality assessment of the therapeutic product at the point of care are very much in demand. In this article, we apply chemometrics based analysis of Raman spectra towards quantitative prediction of protein aggregation in lyophilized biotherapeutic products. Two commercially available therapeutic proteins, erythropoietin (EPO) and human growth hormone (HGH), have been used to demonstrate the applicability of the proposed approach. Thermally induced protein aggregation was monitored by size exclusion chromatography as well as Raman spectroscopy with a 785 nm wavelength laser. Partial least square (PLS) regression was used to analyse the Raman spectra and create a model for quantitative determination of aggregate. Satisfactory performance was observed with both EPO and HGH with R² of 0.91 and 0.94, cross-validation correlation coefficient of 0.85 and 0.89, and Root Mean Square Error computed from cross calibration (RMSEcv) of 5.25 and 1.92, respectively. The developed approach can enable rapid and accurate assessment of aggregation in lyophilized samples of biotherapeutic products. The study also demonstrates novel use of Raman spectroscopy for protein quantification through a vial.