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Continuous integrated bioprocessing has elicited considerable interest from the biopharma industry for the many purported benefits it promises. Today many major biopharma manufacturers around the world are engaged in the development of continuous process platforms for their products. In spite of great potential, the path toward continuous integrated bioprocessing remains unclear for the biologics industry due to legacy infrastructure, process integration challenges, vague regulatory guidelines, and a diverging focus toward novel therapies. In this article, we present a review and perspective on this topic. We explore the status of the implementation of continuous integrated bioprocessing among biopharmaceutical manufacturers. We also present some of the key hurdles that manufacturers are likely to face during this implementation. Finally, we hypothesize that the real impact of continuous manufacturing is likely to come when the cost of manufacturing is a substantial portion of the cost of product development, such as in the case of biosimilar manufacturing and emerging economies.

Aim: The aim of the study was to implement low-power approximate arithmetic circuits for IoT devices. Background: Information transmitted via electronic media is exposed to security threats. With the advancement of internet technology, the devices linked to the internet are growing, leading to the Internet of Things (IoT). Objective: IoT and big data are the prominent computing paradigms that employ approximate computing. It takes the benefit of various applications' error-tolerable features to lower the amount of resources necessary to deliver a specific degree of computation quality. An IoT device has to receive and transmit a lot of data. If this data size can be reduced by approximate computing, then a lot of power can be saved, which provides the dual benefit of data protection and power consumption. Methods: The approximated adder and multiplier using AIF is proposed that helps in the reduction of power consumption and security threats that occur in IoT devices. Result: The proposed approximated adder and multiplier consumes 2.81% to 32.95% less power as compared to conventional technique. Conclusion: For the protection of data communication in IoT devices from security threats, approximate arithmetic circuits play a fundamental role. To attain this issue, in this paper, authors have proposed approximate adder and multiplier using AIF, which also provides reduction in power consumption. The proposed circuits can be used as a basic block for security purposes in IoT devices. In future, approximation algorithms will be implemented for mitigation of security threats.

Handling long-term dynamic variability in harvest titer is a critical challenge in continuous downstream manufacturing. This challenge is becoming increasingly important with the advent of high-titer clones and modern upstream perfusion processes where the titer can vary significantly across the course of a campaign. In this paper, we present a strategy for real-time, dynamic adjustment of the entire downstream train, including capture chromatography, viral inactivation, depth filtration, polishing chromatography, and single-pass formulation, to accommodate variations in titer from 1-7 g/L. The strategy was tested in real time in a continuous downstream purification process of 36 h duration with induced titer variations. The dynamic control strategy leverages real-time NIR-based concentration sensors in the harvest material to continuously track the titer, integrated with an in-house Python-based control system that operates a BioSMB for carrying out capture and polishing chromatography, as well as a series of pumps and solenoid valves for carrying out viral inactivation and formulation. A set of 9 different methods, corresponding to the different harvest titers have been coded onto the Python controller. The methods have a varying number of chromatography columns (3-6 for Protein A and 2-10 for CEX), designed to ensure proper scheduling and optimize productivity across the entire titer variation space. The approach allows for a wide range of titers to be processed on a single integrated setup without having to change equipment or to re-design each time. The strategy also overcomes a key unexplored challenge in continuous processing, namely hand-shaking the downstream train to upstream conditions with long-term titer variability while maintaining automated operation with high productivity and robustness.

Approximate computing is gaining grip as a computing paradigm for computer vision, data analytics, and image/signal processing applications. In the era of real-time applications, approximate computing plays a significant role. In many computers including digital signal processors (DSP) and a microprocessor, adders are the main element for the implementation of signal processing applications and digital circuit design. The major problem for addition is the propagation delay in the carry chain. As the bit length of the input operand increases, the length of the carry chain increases. To address the carry propagation problem in digital systems, the most efficient adder architectures for VLSI implementation are classified as a parallel prefix adder (PPA) structure. In this paper, a novel methodology to implement and synthesize different adders (non-speculative and speculative) for any ASIC-based system is proposed. The proposed hybrid Han-Carlson and Kogge-stone speculative adders show improved performance (low power and delay) over the state-of-the-art approximate adders. If the approximation fails, then the proposed efficient error correction technique is activated. The proposed speculative H_C adder results in a 23.79% speed improvement over the proposed K_S adder, and 23.86% of energy is saved. The proposed architectures were synthesized for an operand bit length of 16 bits. Finally, the proposed adder is validated for an error-tolerant image processing application resulting in 41.2 dB PSNR.

Control of single pass tangential flow ultrafiltration (SPTFF) is crucial for continuous manufacturing of monoclonal antibodies (mAbs). Integrating SPTFF technology into continuous manufacturing trains requires successful resolution of several challenges that arise due to the complexity of mass transfer interactions across multi-membrane configurations, the significant effect of feed material attributes and process variability on flux, and the need for advanced scheduling. In this paper, we propose a real-time, automated monitoring and control strategy for SPTFF in continuous processing of mAbs. The approach leverages a previously developed model for predicting the VCF across an SPTFF module based on the gel polarization model of protein ultrafiltration. A distributed control system (DCS) architecture was created for integrating the monitoring sensors and control elements, including NIRS sensors for concentration monitoring, as well as weighing balances, pressure sensors, pumps, and valves. Two different SPTFF control strategies were developed, firstly for final formulation of the drug product into the drug substance (ultrafiltration and diafiltration), and secondly for in-line concentration between two chromatography steps. Case studies were designed with 15 runs to test the strategy with a range of deviations induced in the feed and process conditions. The retentate concentration was controlled to within 10% of the target value in all runs. The combination of real-time sensor data and model-based control effectively enabled automated and tightly controlled operation of the SPTFF step and is a key enabler of quality by design in continuous mAb manufacturing.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Conjugation of protein therapeutics with polymers like polyethylene glycol (PEG) has been shown to increase their therapeutic efficiency. However, manufacturing of PEGylated drugs requires an additional, carefully controlled reaction step after purifying the protein, followed by further purification of over- and under-PEGylated variants. In this work, we have used a combined spectroscopic and statistical approach for monitoring and control of the PEGylation reaction for G-CSF using near infrared spectroscopy (NIRS). An online NIRS probe deployed in the reaction vessel has been used to track conversion of G-CSF into monoPEGylated and multiPEGylated forms using calibrated partial least squares regression models on the NIRS spectra which are collected in real time every 3 s. A pH probe integrated with a peristaltic pump facilitates automated quenching of the reaction at the targeted time. The NIRS spectra have also been used to build a batch evolution model for the reaction from end-to-end, including the addition of the reactants to the reaction vessel, the progress of the reaction for 70 min, and the final quenching with Tris base. Online spectra are compared against the statistical process control charts of the batch evolution model in real time to detect deviations as soon as they occur. The system was demonstrated for four common deviations in the PEGylation process, namely: delayed quenching time, wrong concentration of reducing agent added, wrong PEG to G-CSF ratio, and wrong sequence of addition of reactants. The system was able to identify all four deviations in real time and alert the operator to take control actions. The PAT approach suggested here embraces the quality by design framework and can be generalized for manufacturing scale monitoring and control of different biotechnology reactions with spectroscopic signatures.

Background: Seed yield is a very important trait for selection but shows low heritability and hence is difficult to improve. Since the productivity of linseed (Linum usitatissimum L.) in India as well as Himachal Pradesh is low in comparison to other major linseed growing countries and states, improvement in cultivars for grain yield is a must. Therefore, the present study was aimed for studying the character associations in linseed genotypes for seed yield over locations. Method: The experiment was conducted during rabi 2019-2020. The experimental material for the present investigation comprised of 52 linseed genotypes grown over three locations in Himachal Pradesh i.e. Linseed Experimental Farm, CSK HPKV, Palampur (1290 m amsl), Shivalik Agricultural Research and Extension Centre, Kangra (700 m amsl) and Hill Agricultural Research and Extension Centre, Dhaulakuan (468 m amsl). Randomized block design with three replications was used. Phenotypic and genotypic correlation coefficients were worked out as per the procedure outlined by Burton and De Vane (1953) and Johnson et al. (1955). Direct and indirect effects of component traits on grain yield were worked out using correlation coefficient of various traits as suggested by Wright (1921) and elaborated by Dewey and Lu (1959). Result: Correlation studies indicated highly significant positive correlation for seed yield with 1000 seed weight (0.965**) followed by harvest index (0.801**), secondary branches (0.585**) and a significant correlation with biological yield (0.269**). Seed yield exhibited a non-significant positive correlation with seeds per capsule. However, a negative significant correlation was observed for seed yield with days to 50 per cent flowering and number of primary branches. The path coefficient analysis indicated that 1000 seed weight exhibited maximum positive direct effect with seed yield (0.741) while others had a low direct effect. The significant positive correlation of number of secondary branches and harvest index with seed yield was mainly due to indirect effect via 1000 seed weight indicating that 1000 seed weight is the most important trait for the improvement of grain yield in linseed as per the present study.

Nanoscale ICs required components with increased speed and less consumption of power. The trade-off between speed, area, and power is an important task. For the optimization of these parameters, approximate computing is used. In this paper, the approximate high-order compressor is implemented which is further used in partial product reduction of the multiplier. It is important to increase the computational parameters of the multiplication process. The performance of the multiplier increases by using compressors. In this paper, the authors proposed 9:2 compressor which helps in the reduction of the complexity of the circuit and increases the performance of the multiplication block. By applying compressors in the multiplication block, an energy-efficient multiplier is implemented. An energy-efficient multiplier can be further used in error-tolerant applications. The proposed approximated multiplier consumes 43.93% low power and area utilization 49.52 % less as compared to the exact multiplier. All the simulation is carried out in VIVADO 2018.3 using Verilog HDL coding.

Heart sound analysis has been used as a tool for many cardiovascular disease diagnosis. In this study, wavelet packet energy based features have been proposed to detect the abnormal heart sound in phonocardiogram(PCG) signal. The heart sound dataset of 2016 PhysioNet/CinC challenge is utilized for developement of the model. The effectiveness of proposed features are assessed by the machine learning techniques: random forest(RF), support vector machine (SVM) and multilayer perceptron (MLP). The proposed wavelet packet cepstral coefficients (WPCC) features achieved an average accuracy of up to 84.88% on the unseen test dataset using SVM classifier. The proposed features have shown better performance compare to time and frequency domain features. Automated analysis of phonocardiogram signal will provide better clinical information to doctors to diagnose different heart abnormalities.

A real time mechanistic model-based control strategy is demonstrated for in-line pH adjustment post-capture chromatography and prior to viral inactivation for continuous processing of monoclonal antibodies. At this point in the process, tight control of pH is essential, as pH fluctuations above 3.5 can result in incomplete viral inactivation, while fluctuations below 3.5 can lead to significant aggregate formation. The present approach predicts the pH profile during the transition phase between chromatography wash and elution steps by modelling the process stream at the column outlet as a mixture of two independent buffer systems. Control of pH in this transition phase is a critical consideration in capture chromatography as a significant amount of mAb material is eluted at this time. The model inputs are buffer concentrations, flow rates, and theoretical pKa values, along with cleaning step conductivity profiles which are readily available from a typical process chromatography equipment. The utilization of the most recent cleaning cycle data as an input to the model allows sensitive calibration to the individual process at hand on a column-to-column basis. The model is able to accurately predict the pH profile throughout the elution, as well as calculate the flow rate of the acid (titrant) required at each time point to maintain the pH consistently at 3.5±0.2. The strategy is demonstrated for various buffers, columns, operating conditions, and process deviations in a three-column continuous process, and is a useful and simple approach for achieving robust control of pH at this critical point in the continuous train.

Process analytical technology is gaining interest in the biopharmaceutical industry as a means to enable consistency in processing and thereby in product quality via process control. Protein refolding is known to be significantly impacted by critical process parameters and feed material attributes including composition and pH of the solubilisation and refolding buffers. Hence, to achieve robust process control and product quality, these attributes and parameters need to be monitored. This paper presents an approach towards statistical process control and monitoring of protein refolding, from buffer preparation to refold quenching, during manufacturing of therapeutic proteins from Escherichia coli based systems. The proposed approach utilises measurements of online redox potential, temperature, and pH for development of a statistical model. The model has then been integrated with LabView to permit real-time monitoring of the refolding process. The proposed system has been demonstrated to successfully identify process deviations and thereby enable process control for manufacturing product of consistent quality.

Surge tanks are critical but often overlooked enablers of continuous bioprocessing. They provide multiple benefits including dampening of concentration gradients and allowing process resumption efforts in case of equipment failure or unexpected deviations, which can occur during a continuous campaign of weeks or months. They are also useful in enabling steady-state operation across a continuous train by facilitating mass balance between unit operations such as chromatography which have periodic loading and elution cycles. In this paper, we propose a design of a system of surge tanks for a monoclonal antibody (mAb) production process consisting of cell culture, clarification, capture chromatography, viral inactivation, polishing chromatography, and single-pass ultrafiltration and diafiltration. A Python controller has been developed for robust control of the continuous train. The controller has four layers, namely data acquisition, process scheduling, deviation handling, and real-time execution. A set of general guidelines for surge tank placement and sizing have been proposed together with process control strategies based on the design space of the individual unit operations, failure modes analysis of the different equipment, and expected variability in the process feed streams for both fed-batch and perfusion bioreactors. The control system has been successfully demonstrated for several continuous runs of up to 36 h in duration and is able to leverage surge tanks for robust control of the continuous train in the face of product variability as well as process errors while maintaining critical quality attributes. The proposed set of strategies for surge tank control are adaptable to most continuous processing setups for mAbs, and together form the first framework that can fully realize the benefits of surge tanks in continuous bioprocessing.

Approximate computing provides the tradeoff between the accuracy, the speed as well as power consumption. Approximate adders and other logical circuits can reduce hardware overhead. In this paper non-speculative and speculative parallel prefix adder is proposed and makes it more reliable to be used in applications where high speed circuits are required. If there is misprediction of result in speculative adder then error-correction is activated in the next clock cycle. Speculation is a process in which approximation is done. Approximate computing is widely used in the current scenario. The speculative adder reduces the critical path and provides the trade-off between reliability and performance. Proposed speculative parallel prefix adder results in 8.204ns delay which shows 36.87%, 2.35%, 26.32 % improvement in comparison to conventional NSA, proposed NSA, and conventional SA. Architecture is implemented for 16-bit operand length and used is an FPGA-based processing application.

Dead end filtration is a critical unit operation that is used for primary and secondary clarification during manufacturing of both microbial and mammalian cell based biotherapeutics. Dead end filtration is conventionally done in batch mode and requires filter pre-sizing using extensive scouting studies, along with filter over-sizing before deployment to handle potential variability. However, continuous manufacturing processes require consistent use of dead-end filtration over weeks or months, with potential unpredictable variations in feed stream attributes, which is a challenge currently facing the industry. In this work, a dead-end filtration skid is designed for continuous depth filtration, incorporating multiple small-sized filters along with turbidity, and pressure sensors with immediate switching to a fresh filter whenever turbidity or pressure breakthrough above a pre-determined cut-off is detected in real time. The skid has been successfully tested for manufacturing of granulocyte colony stimulating factor from Escherichia coli, human serum albumin from Pichia pastoris, and a monoclonal antibody therapeutic from CHO cells. The proposed skid can be directly applied for any dead-end filtration application with minimal prior scouting studies or sizing calculations for scale-up. It is a useful solution for continuous processing trains where the nature of the feed, such as its turbidity or host cell proteins content, may change over long continuous campaigns, rendering previous sizing calculations inaccurate. The skid also allows significant cost savings by eliminating the sizing safety factor of 1.5–2x which is generally added before filter deployment at manufacturing scale.

Adders play a crucial role and are one of the fundamental building blocks in application like signal processing. In the modern scenario, integrated circuits are majorly used in which the requirement of adder module is vital. This paper addresses various forms of adders which include Ripple-carry (RC), Carry-skip (CSk), Carry-lookahead (CL) and Kogge-stone(KS) adders. These adders can meet certain design constraints in digital VLSI circuits, such as speed and area. For various bit-lengths, a new KS adder is proposed in this paper which was analyzed on the basis of delay. The proposed adder with 8, 16, 32, 64 and 128 bit-length results in 7.487ns, 9.248ns, 10.295ns, 7.259ns and 6.814ns delay respectively.84.11% improvement has been analyzed for 128 bit KS adder in comparison with CSk adder, 88.34 % in comparison with RC adder and 88.83% in comparison with CL adder. The proposed KS architecture can be used in time critical FPGA based signal processing applications.

Energy harvesting is a very attractive method in all the applications where battery replacement or recharging is difficult. We can harvest energy using environmental vibrations caused because of frequencies that are present naturally in our environment. Vibration energy can scavenge energy from mechanical vibrations to energies low power electronic devices. Low frequencies energy harvesting system can begin to replace batteries in certain wearable devices. As piezoelectric vibration based energy harvester and do not require external voltage and also they are not expensive making them feasible alternative to implement energy harvesting. In this paper we are investigating a bimorph piezoelectric cantilever geometry by using COMSOL Multiphysics 5.2. Material used is Lead Zirconate Titanate (PZT 5A), and stainless steel.

Formulation of biotherapeutics using single pass tangential-flow filtration (SPTFF) is a critical step in continuous manufacturing processes for many drugs including monoclonal antibodies (mAbs). The concentration of the mAb in the final formulation is a critical quality attribute which affects safety and efficacy and is determined by the concentration factor achieved by the process material stream in a single pass through an SPTFF module. Modern SPTFF modules consist of multiple smaller membranes connected in different series and parallel configurations to increase the overall membrane area and residence time of the process material in order to achieve high concentration factors in a single pass. The present work leverages the gel polarization model of protein ultrafiltration to develop a model for the permeate flux vs. time profile of a single membrane inside an SPTFF module based on three key resistances, namely the boundary layer resistance, resistance of the deposited protein layer over time, and intrinsic membrane resistance. Correlations are developed to predict the key mechanistic parameters across the typical operating range of flow rate, bulk concentration and transmembrane pressure in downstream mAb processing steps. The single membrane model is then used as a building block to model complex SPTFF configurations with diverse tree structures, and to facilitate in-silico design of customized SPTFF configurations based on operating targets.