BioSpace Movers and Shakers

ALEX KEOWN | April 26, 2019

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It didn’t take long for Novartis to tap a new chief executive officer for its generics subsidiary Sandoz. GlaxoSmithKline veteran Richard Saynor, who spent five years with Sandoz earlier in his career, was tapped as the new CEO of Sandoz. For the past nine years, Saynor has been with GSK, most recently serving as senior vice president and global head of classic and established medicines at the U.K. pharma giant. However, before beginning with GSK back in 2010, Saynor spent five years with Sandoz, overseeing operations in Asia, Turkey and Canada. Saynor will take over the helm of Sandoz following the abrupt departure of former CEO Richard Francis. Francis resigned from his role in March as parent company Novartis undertakes a year-and-a-half-long transformation of its Sandoz business unit.

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AI and Biotechnology: The Future of Healthcare Industry

Article | January 20, 2021

Artificial intelligence has grasped the foundation in biotech. It can have the most innovative impact on biotechnology. AI has already established its presence in our day-to-day life. AI has made the existence of self-driving cars possible. Likewise, the benefits and quality that it can contribute to biotech can also be felt. With AI, bio technicians will be able to enhance virtual screening, overlook preliminary datasets from clinics, and decipher an enormous amount of information. It can also help in improving the medication process by gathering and analyzing every bit of information. The Significance of AI in Biotechnology In the past few years, the application of artificial intelligence in the biotechnology industry has shifted from being sci-fi to sci-fact. A vast number of biotech companies like Deep Genomics are adopting AI for making data-driven decisions and use analytics tools to work efficiently. Unlike the AI robots in sci-fi that are ready to take over the world. AI designed for biotech has been designed to solve certain problems or complete a bunch of tasks by using automated algorithms. The aim of AI technology for biotech is to collect insights along with hidden patterns from large amounts of data. All the different industries of biotech including agriculture, animal, medical, industrial, and bioinformatics are gradually being affected by artificial intelligence. Moreover, the biotech industry is realizing that AI enables them some of the important strength to their business, including: Expanding accessibility Cost-effectiveness Critical predictions Efficient decision-making Research centers like PwC have also estimated output of $15.7 trillion by 2030 solely with AI contribution in industries. A survey revealed that about 44% of life science experts are using AI for R&D activities, as well. Use of AI in Biotechnology Altering Biomedical and Clinical Data So far the most developed use of AI is its ability to read voluminous data records and interpret them. It can prove to be a life-save for bio technicians who would have to examine that much data from research publications by themselves for the validation of their hypothesis. With the help of AI, clinical studies of patients will also become easier as all the examination reports and prescriptions will be stored in one place for cross-reference. Furthermore, it will also help in blending and fetching data into usable formats for analysis. Test Result Prediction Through trial and error, AI along with machine learning can help in predicting the response of the patient to certain drugs to provide more effective outcomes. Drug Design & Discovery AI plays a vital role whether it’s designing a new molecule or identifying new biological targets. It helps in identifying and validating drugs. It reduces the cost and time spent on the entire drug trial process and reaches the market. Personalized Medications for Rare Diseases With the combination of body scan results, patients’ body and analytics, AI can also help in detecting dangerous diseases at an early stage. Improving Process of Manufacturing To improve the process of manufacturing in biotechnology, AI offers a wide range of opportunities. It controls quality, reduces wastage, improves useability, and minimizes the designing time. Moving Towards AI-Enhanced Biotech Future Ever since the concept of artificial intelligence has arrived, being curious by nature, humans have started working towards achieving this goal. It has been growing at a fast pace while showing unbelievable growth and achievements at times. In comparison to the traditional methods used in the biotechnology industry, AI-based methods seem more reliable and accurate. In the upcoming years, it will show its success by improving the quality of health people have. You can also develop your AI-based application or know more about it by taking IT consultations.

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Closing bacterial genomes from the human gut microbiome using long-read sequencing

Article | February 12, 2020

In our lab, we focus on the impact of the gut microbiome on human health and disease. To evaluate this relationship, it’s important to understand the particular functions that different bacteria have. As bacteria are able to exchange, duplicate, and rearrange their genes in ways that directly affect their phenotypes, complete bacterial genomes assembled directly from human samples are essential to understand the strain variation and potential functions of the bacteria we host. Advances in the microbiome space have allowed for the de novo assembly of microbial genomes directly from metagenomes via short-read sequencing, assembly of reads into contigs, and binning of contigs into putative genome drafts. This is advantageous because it allows us to discover microbes without culturing them, directly from human samples and without reference databases. In the past year, there have been a number of tour de force efforts to broadly characterize the human gut microbiota through the creation of such metagenome-assembled genomes (MAGs)[1–4]. These works have produced hundreds of thousands of microbial genomes that vastly increase our understanding of the human gut. However, challenges in the assembly of short reads has limited our ability to correctly assemble repeated genomic elements and place them into genomic context. Thus, existing MAGs are often fragmented and do not include mobile genetic elements, 16S rRNA sequences, and other elements that are repeated or have high identity within and across bacterial genomes.

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Making Predictions by Digitizing Bioprocessing

Article | April 20, 2021

With advances in data analytics and machine learning, the move from descriptive and diagnostic analytics to predictive and prescriptive analytics and controls—allowing us to better forecast and understand what will happen and thus optimize process outcomes—is not only feasible but inevitable, according to Bonnie Shum, principal engineer, pharma technical innovation, technology & manufacturing sciences and technology at Genentech. “Well-trained artificial intelligence systems can help drive better decision making and how data is analyzed from drug discovery to process development and to manufacturing processes,” she says. Those advances, though, only really matter when they improve the lives of patients. That’s exactly what Shum expects. “The convergence of digital transformation and operational/processing changes will be critical for the facilities of the future and meeting the needs of our patients,” she continues. “Digital solutions may one day provide fully automated bioprocessing, eliminating manual intervention and enabling us to anticipate potential process deviations to prevent process failures, leading to real-time release and thus faster access for patients.” To turn Bioprocessing 4.0 into a production line for precision healthcare, real-time release and quickly manufacturing personalized medicines will be critical. Adding digitization and advanced analytics wherever possible will drive those improvements. In fact, many of these improvements, especially moving from descriptive to predictive bioprocessing, depend on more digitization.

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Cell Out? Lysate-Based Expression an Option for Personalized Meds

Article | February 18, 2020

Cell-free expression (CFE) is the practice of making a protein without using a living cell. In contrast with cell line-based methods, production is achieved using a fluid containing biological components extracted from a cell, i.e., a lysate. CFE offers potential advantages for biopharma according to Philip Probert, PhD, a senior scientist at the Centre for Process Innovation in the U.K.

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Kapa Biosystems

Kapa Biosystems is a life science reagents supplier that employs proprietary, directed evolution technologies to optimize enzymes for PCR, real-time PCR, next generation sequencing and molecular diagnostic applications.

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