Aizon

Pharmaceutical manufacturers are under increasing pressure to fast-track technological innovation to improve their manufacturing operations in order to adapt to new approaches like ‘Industry 4.0’ and continuous manufacturing, combat the growing and intense competition and keep up with the increasing demand for personalized medicine and therapies. But with an industry with the world’s tightest regulations and complex value chains, what are the correct steps to reaching operational intelligence in a GxP compliant manner with end-to-end data integrity?

How Industry 4.0 technologies like artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and data lakes used in conjunction can help you integrate and harmonize your data providing a holistic view of your operational environment

How to do compliant data acquisition and how safe it is to use AI/ML in a GxP environment and ensure data integrity, encryption, certification, and other key aspects of data management

How adopting these technologies result in huge benefits such as decreased downtime and increased productivity

Quality Assurance Managers and Regulatory Managers

CIO, Chief Data Officers, Chief Digital Officers

Pharma and CDMO Manufacturing and Operations


Chief Science Officer and Co-founder
Aizon

Toni Manzano is a co-founder and Chief Science Officer of Aizon, a SaaS company that is transforming manufacturing operations with the use of AI and IIoT technologies for pharmaceutical and biotech companies. For over two decades, he has led software projects for international pharmaceutical companies covering the entire production process and supply chain. Today, Toni is a part of the scientific committee with PDA Europe and with the AI Xavier Manufacturing team. He worked as a researcher at the University of Barcelona as a physicist and teaches big data and artificial intelligence in postgraduate courses at the UAB. He is also a member of the Science Experts in the Spanish Parliament on big data and artificial intelligence. He has written numerous articles and holds a dozen international patents related to the encryption, transmission, storage, and processing of large volumes of data for regulated environments in the cloud.

Cristina is a scientist with a passion for IT and new technologies. She has a bachelor’s degree in chemistry and holds a Ph.D. in science and technology applied to chemistry with special focus in data analysis (chemometrics). She started her career in pharma in the UK working for AstraZeneca as a process analytical technology (PAT) specialist using spectroscopy to provide process optimization and understanding for pharmaceutical processes in both early and late stage projects enabling scale-up reducing risks. She joined Aizon in early 2018 in quality assurance providing guidelines and training to developers about quality and compliance in pharmaceutical environments. Currently, she is the product owner and ensures the Pharma 4.0 technologies developed are ready to be used in GxP environments.

Webcasts

Monday December 20, 2021 at 11am EST | 10am CST | 8am PST 
Pharmaceutical manufacturers are under increasing pressure to fast-track technological innovation while remaining compliant. What are the correct steps to reaching operational intelligence with end-to-end data integrity?

Why Biotech and Pharma Need GxP for their Digital Transformation Projects Now

Articles

Creating a Digital Twin Strategy Using Artificial Intelligence

This paper explores the most important data challenges experienced by the rapidly modernizing pharmaceutical industry and proposes a GxP-compliant, ready-made application as a solution designed to increase efficiency in upstream processes, while maintaining data integrity and compliance.

Improve Yield and Predict Anomalies in Your Bioreactor

https://www.pharmtech.com/pt_w/artificial

Traditionally, biotech processes have been investigated by checking key parameters individually, which inhibits efficiency and reduces the real view of how the system interacts. The reality, though, is that bioprocessing is complex and the interactions between factors are continuous and unpredictable. To truly understand and predict biosystems, manufacturers need to employ multivariate analysis using artificial intelligence (AI). In this webinar, Aizon experts discuss how digital twins, powered by AI, can reproduce conditions happening in physical assets to identify and optimize manufacturing process variables, predict outcomes, and empower bio/pharma companies to act.

Learn the value in creating digital twins optimized for the holistic process in pharma and biotech manufacturing

Understand the fundamental elements to know when creating a digital twin for predictive insights

Ensure efforts on developing digital twins are future-proofed for challenges like CPV and continuous manufacturing

Toni Manzano is a co-founder and CSO of Aizon, a software as a service company that is transforming manufacturing operations with the use of artificial intelligence (AI) and industrial internet of things technologies for pharmaceutical and biotech companies. For over two decades, Manzano has led software projects for international pharmaceutical companies, covering the entire production process and supply chain.

Today, Manzano is part of the scientific committee with PDA Europe and with the AI Xavier Manufacturing team. He worked as a researcher at the University of Barcelona as a physicist and teaches big data and artificial intelligence in postgraduate courses at UAB. He is also a member of the Science Experts in the Spanish Parliament on big data and artificial intelligence. Manzano has written numerous articles and holds a dozen international patents related to the encryption, transmission, storage, and processing of large volumes of data for regulated environments in the cloud.


Solution Engineer and Industry Expert
Aizon

Luiza Mukaeda is an engineer with a master’s degree in business administration (MBA) who is excited about the powerful innovative approaches to achieve agile problem solving, especially in the manufacturing environment. Luiza has a bachelor’s degree in chemical engineering by the University of São Paulo, and an MBA by the Herriot-Watt University, in Scotland.

After kicking off her career in the oil and gas industry, Luiza has discovered her passion to contribute to the mission of delivering life-saving products as part of the pharmaceutical industry. She spent several years in the industry and collaborated on positions like process engineering, project manager, and engineering manager.

In 2020, Luiza joined Aizon to continue driving her passion from a different perspective. She is here to empower the company’s vision on supporting customers to find the best opportunities towards business value, so their mission is secured.

Monday, August 23, 2021 at 2pm EDT | 1pm CDT | 11am PDT
What do Plato, Roombas, and digital twins for pharma manufacturing have in common? Learn how digital twins, powered by artificial intellingence, can reproduce conditions happening in physical assets to identify and optimize manufacturing process variables, predict outcomes, and empower to bio/pharma companies take action.

Using Artificial Intelligence in a Digital Twin Strategy

Podcasts

Artificial Intelligence (AI) is changing how biotech and pharmaceutical companies operate. Increased competition and an enhanced regulatory landscape are forcing drug manufacturers to eliminate waste, create efficiencies and squeeze every last bit of productivity out of existing processes. The benefits can be enormous. A three
percent increase in usable yield could increase revenue by millions of dollars. Pharma organizations that take advantage of AI/ML in GxP environments get better visibility into their manufacturing operations and can thereby optimize yields and predict anomalies thereby providing more to the market. We’ll explore where the industry is in its adoption of AI, what the challenges are in implementation that keep the pharma industry behind other industries, and explore case studies of those intrepid organizations who have worked through the challenges to implement and reap the benefits.
