#Manufacturing Science and Technology (MSAT)

[techtrends-today]

Transforming Biotechnology with AI

The Growing Influence of Biotechnology

Biotechnology has become a pivotal force in various industries, particularly in healthcare and pharmaceuticals. With the ability to manipulate biological systems for the development of new drugs, therapies, and sustainable production methods, its applications are vast and impactful. At the heart of this progress is AI, which is enhancing efficiency, accuracy, and scalability in biotechnology processes.

Successful Scale-Up Examples

Moderna's COVID-19 Vaccine Production

Moderna successfully scaled up COVID-19 vaccine production by using AI algorithms to quickly identify promising mRNA sequences, speeding up development and enabling rapid large-scale production. This highlights AI's role in responding effectively to global health crises.

Ginkgo Bioworks' Synthetic Biology Advancements

Ginkgo Bioworks has made significant strides in synthetic biology by using AI to automate the design and testing of microorganisms. Their platform, which integrates AI with robotics, has enabled the efficient scale-up of synthetic biology projects from lab experiments to industrial applications. This has opened new avenues in producing bio-based products, from fragrances to agricultural strains, showcasing the versatility of AI in biotechnology.

Amgen's Biologic Drug Manufacturing

Amgen uses AI to enhance biologic drug manufacturing by implementing predictive analytics and process optimization, which streamlines production, reduces costs, and improves yield while maintaining quality in biopharmaceutical environments.

Biopharmaceutical Manufacturing Applications

Monoclonal Antibody Production

AI has revolutionized monoclonal antibody production, crucial for treating cancers and autoimmune diseases, by optimizing development and purification processes. Companies like Genentech and Bayer lead in enhancing production efficiency and efficacy through AI.

Outsourcing Manufacturing

AI is crucial in outsourcing biopharmaceutical manufacturing by enhancing quality and compliance. CMOs like Lonza and WuXi AppTec use AI to monitor production in real-time, detect anomalies, and ensure regulatory adherence, boosting efficiency, transparency, and control.

Data-Driven Process Optimization

AI-driven data analysis is revolutionizing biopharmaceutical manufacturing by optimizing processes. It analyzes production data to recommend improvements, enhancing yields, reducing waste, and shortening cycles. Pfizer's use of AI in gene therapy production exemplifies these advancements.

The Future of Biotechnology with AI

Enhanced Predictive Modeling

The future of biotechnology lies in the continued advancement of predictive modelling through AI. By refining these models, companies can anticipate and mitigate potential issues before they arise, leading to more robust and reliable production processes. This proactive approach will be essential as the complexity of biopharmaceutical products continues to grow.

Personalized Medicine

AI is transforming personalized medicine by tailoring treatments to patients' genetic profiles. Companies like IBM Watson Health are leading this innovation with AI algorithms that analyze patient data for optimal treatment identification, promising improved outcomes and a healthcare revolution.

Sustainable Biomanufacturing

AI technologies are enhancing sustainability in biomanufacturing by optimizing resource use, reducing waste, and minimizing environmental impact. For instance, AI-driven improvements in biofuel fermentation processes offer more efficient and eco-friendly fuel alternatives.

FAQs

Q1: How does AI enhance biopharmaceutical manufacturing?

A1: AI enhances biopharmaceutical manufacturing by optimizing processes, improving yield, ensuring quality control, and enabling predictive maintenance.

Q2: What are some successful examples of AI in biotechnology?

A2: Notable examples include Moderna's rapid COVID-19 vaccine production, Ginkgo Bioworks' synthetic biology advancements, and Amgen's biologic drug manufacturing improvements.

Q3: How can AI contribute to personalized medicine?

A3: AI contributes to personalized medicine by analyzing patient data to tailor treatments to individual genetic profiles, improving the efficacy and safety of therapies.

World Biomanufacturing Forum Overview

The World Biomanufacturing Forum, highlighted on the Leadvent Group's website, is a key meeting point for stakeholders in the biomanufacturing sector. This event promotes the exchange of ideas and practices among industry leaders, researchers, and policymakers. It provides a platform to discuss the latest in biomanufacturing technologies, regulatory updates, and effective production scaling strategies. Attendees can join expert-led sessions and networking activities, fostering collaborations to advance the industry. With a focus on cutting-edge developments, the forum is vital for those aiming to lead in bioprocessing and production efficiency.

[jobrxiv]

Sr Associate Scientist Insight Global See the full job description on jobRxiv: https://jobrxiv.org/job/sr-associate-scientist/?feed_id=49535 #ScienceJobs #hiring #research Andover #UnitedStatesUS #ResearchAssistant #ResearchTechnician #Researcher #Scientist

[tridiagonal]

Solution for Manufacturing Data Analytics and Data Science Services

Tridiagonal Solutions Process Analytics Group supports Data Science and Manufacturing Data Analytics initiatives of the organizations through its Guided Analytics Services & Solutions Framework. This framework enables organizations to devise KPI-based analytics strategy and selection of the right approach/methods/solutions for successful implementation at an enterprise level.  

Read more here: https://dataanalytics.tridiagonal.com/consulting/  

We fill the gap in terms of skillsets, know how and best practices in the data analytics space and help companies to get started in their data analytics journey. Our multi-skill teams consist of Domain experts in Chemical / Mechanical engineering with strong industry exposure in Oil & Energy, Chemicals, Pharmaceuticals, Metals & Mining, Cement sectors. The essential combination of data engineers, data scientists, and application engineers is instrumental in implementing data analytics solutions at our customer's end. We have a suite of solutions (Data analytics platform), which we implement as a part of our services.

Today most of the manufacturing data analytics is in the form of monitoring (what is happening) the current state of the system/ process or historical data investigation (What has happened); trying to understand the events. The basic application of statistics (such as covariance/ correlation) helps in doing deterministic analysis such as root cause analysis of the past events and identifying ‘Why’ such events happened.  

There are different applications of data science and analytics, which can help in performing various levels of analytics from diagnostics to probabilistic to augmented data modeling. The Machine learning techniques and application of the right methods can unlock the data and give meaningful insights. However, to realize the benefits of this technology, the organizations need to have the right ‘Analytics Strategy’, skills, methods, and right solutions.

Following are the service offerings

Core Data Science Group - ML Modeling Center

Analytics implementation Strategy – Roll-out plan with Returns on Analytics (ROA)

Building Analytics & Data-driven models for various applications (processes) – Model construction, application or validation and implementation – Lab/ R&D, Process Dev., Manufacturing, Supply chain, etc.

Department-level Value creation (Lab, Process R&D/ Dev., Quality, Operations)

Augmented modeling for complex processes (Mechanistic + Data-driven), working with SMEs for advanced analytics (level 4, 5)

Custom Application for a specific purpose – Templatizing Analytics

Model Management & Tuning – Enhancing the value of models

Knowledge-creation and building Analytics Culture / Champions – Analytics Transformation

Center of Analytics (COA) – Management of Data Insights

Extended team for Data Mining and Manufacturing Data Analytics – Managing multiple plants/ assets

Dynamic reports and KPI/dashboards for global department heads (Asset-level/ Process level) – R&D, Quality, Operations, maintenance, CXOs

Working with Plant / Operations head-on Operational effectiveness and productivity analytics

Working with MSAT team on Process & Optimization Projects using Data Modeling & Analytics

Back-end ‘Analytics Production Team’ to the Data Science/ Digitalization leaders

Establishment of Best Practices for Data Analytics

Knowledge-building Workshops

Hands-on training on Data Analytics – Data Preparation & Pre-processing – Data Cleansing, Conditioning, descriptive statistics

Feature identification, reduction, Covariance methods

How to create Data Twin – System / Process representation for data modeling/ Analytics

Model construction, application / validation and implementation – Right Methods (Use case-specific)

Best Practices or Guide & Methods for Manufacturing Data Analytics

Advanced Data modeling techniques on your data and results

Data Analytics Solution (Software) – Implementation

Providing Industry leader – Advanced Analytics Solution

Proof-of Value development for Seeq evaluation

Configuration & Implementation Plan and Global roll-out

Establishing Logical Data Lake using Seeq Cortex Server

Training / Support post-implementation

Custom Application / Dashboards Development using Seeq API/SDK

Remote monitoring and KPI-driven dashboard creation for plants

Ask your query and talk to an expert here @ https://dataanalytics.tridiagonal.com/contact-us/  

[hourlyjobupdates-blog]

MSAT Scientist

Lonza Location : Houston TX US As a member of the MSAT Team (Manufacturing Science and Technology) you are responsible for the successful transfer of processes into production. As such you

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