Join us October 5-7, for a transformative Paperless Lab Academy® (PLA) conference in Orlando, Florida held in collaboration with 20/15 Visioneers. The event will focus on education and learning the skills necessary to achieve next-generation transformation in your labs. Subject Matter Experts who care about improving laboratory processes and the quality of work life will be present and involved. Already booked are experts from AstraZeneca and Takeda.

Today’s laboratories are evolving at an unprecedented pace. The following themes reflect the real-world needs and priorities that R&D organizations must address to thrive. By adopting in-silico-first approaches, researchers can accelerate discovery and boost lab efficiency by over 60%. Robust scientific informatics and effective data management are foundational to these advancements — they form the backbone of in-silico innovation and deeper insights. Building Smart and Smarter labs is the key to making this vision a reality.

Our hands-on labs equip scientists and IT professionals with the essential skills they need to navigate and succeed in today’s dynamic R&D environment.

Event Themes

In-Silico First Science : Using Artificial Intelligence and Machine Learning (AI/ML)to give a head start to your science

Scientific Informatics and Data Management : Your Data is an Asset that  must be organized and contextualized properly

Creating Smart or Smarter Labs:  Advanced automation and robotics will drive model quality data and reproducible science

Hands-On Labs

4 training sessions are programmed by Day 1

1. Building a BPM (Business Process Map) for Your Lab
2. Getting Started with Cheminformatics
3. Prompt Engineering for Scientists
4. Building No-Code LLM Workflows for Your Lab

Registration links

TICKETS: Registration is limited. Secure your admission by registering here and get ready for a new experience that will help you and your organization discover and develop more!

HOTEL: We have negotiated a discounted rate at the beautiful Florida Hotel, centrally located in downtown Orlando. Our block guarantees the lowest room rate and applies for an extended stay if you want to come early or stay late. To book your accommodations click here.

SPONSOR: Put your solutions and services in front of key decision makers. We are offering a range of sponsorship opportunities to maximize your visibility before, during, and after the event. Find out more here.

“The best events are those where experts can share and learn.  This is the culture that PLA® Orlando strives for.  It is a life sciences community event, and importantly an opportunity for you to advance your personal skillset and the capabilities across your organization,” John F. Conway, Founder & Chief Visioneer Officer, 20/15 Visioneers.

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The “Data-driven” here implies much more than just technology and data.  it requires a special mentality,  a leadership attitudes, that makes it possible to take informed decisions by systematically using data to improve business performance.

To lead the data-driven culture, corporations need to first establish a stream of clean, accurate, reliable, and active data feeds reflecting all major business activities. All these qualities are critical for employees to trust the data, the technology, and the tools – any limitation can prove to be the single point of failure in making the data-driven vision happen.

The success of machine learning (ML) and artificial intelligence (AI) applications requires large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality.  ML and AI are highly dependent on the quality of data used to train models. Incomplete, erroneous, Poor data quality can lead to can lead to inaccurate predictions, unreliable model performance and ultimately poor decisions.

This why this session will be focused on the Data quality which is of critical importance especially in the era of Artificial Intelligence and automated decisions. Share real success stories on how the use of data allowed smart decisions with real financial gains for the company

There are many aspects to data quality, including consistency, integrity, accuracy, and completeness. According to Wikipedia, data is generally considered high quality if it is “fit for its intended uses in operations, decision making and planning, and data is deemed of high quality if it correctly represents the real-world construct to which it refers.”

Debabrata Sanyal, Senior General Manager – Corporate Quality Assurance and Digital Automation – MSN Laboratories Private Limited

Debabrata Sanyal is Senior General Manager in Corporate Quality Assurance and Digital Automation in MSN Laboratories Private Limited. He brings 22 years of experience in diversified field of Quality Management predominantly in Quality Assurance, Quality Compliance and Regulatory Affairs. Debabrata previously worked for Regulatory Affairs and Quality System in renowned organisations like Cadila Pharmaceuticals, Torrent research Center, Aurobindo Research Center and MSN Laboratories Private Limited. In his recent assignment at MSN Laboratories Private Limited, Debabrata works for transforming the long-term digitalisation and automation targets into process and solutions.

Use Cases of drug discovery automation and ML models

Samiron Phukan, Senior director Computational (AI/ML) Modelling & Digital Transformation integrated Drug Discovery and Development at Aragen LifeSciences

Samiron Phukan has nearly 20 years of experience in the field of informatics driven solution in drug discovery and development. He had worked in various pharmaceutical companies and CROs in India in the field of drug discovery and development from concept to clinic in various therapeutic areas like oncology, metabolic disorders and anti-infectives using computational modelling and informatics. He had set up the state-of-the-art informatics laboratories in various pharmaceutical companies like Jubilant Biosys, Dr. Reddys’s Laboratories, Lupin ltd. Presently he is heading the CADD/informatics division in Aragen Lifesciences Hyderabad. In addition to his current role of heading the scientific team of computer aided drug design scientist, he is involved digitization, automation and development of various proprietary platform technology using ML tools to aid drug discovery and development.

How communication and data standards enable the automation and data management lifecycle

Burkhard Schaefer, AnIML Task Group Lead, SiLA Consortium

Communication and data standards form the bedrock of efficient laboratory processes. This presentation explores the role of SiLA (Standardization in Lab Automation) and AnIML (Analytical Information Markup Language) in driving automation, data management, and machine learning. SiLA ensures seamless instrument control and workflow orchestration, while AnIML structures data for consistent analysis and collaboration. Learn how the synergy between these standards fuels closed-loop experimentation, enabling optimized parameter adjustments using real-time insights. Through real-world cases, this presentation discusses the impact of standards in shaping modern laboratory practices, fostering innovation, and accelerating research.

Followed by Panel Discussion: How to set standards for the Lab 4.0

DISCOVER FULL PROGRAM HERE

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The Paperless Lab Academy® has just concluded its 10th European edition with a strong program on Artificial Intelligence in GMP bioprocesses, fully automated workflows in bioburden analysis, cybersecurity awareness, standardisation of global LIMS implementation and several parallel discussions in the numerous workshops.

This is the first article of a series to summarise and share the key outcomes from the recent #PLA2023Europe with the PLA community starting with the discussion about the use of Artificial Intelligence in GMP environment.

And therefore, who could be better than Toni Manzano, CSO and co-founder at Aizon, to introduce us carefully to AI so that we can all appreciate the actual real cases implemented in bioproduction processes?

AI is nowadays rather often routine

Toni always likes to remember his audience that the term artificial intelligence was coined as early as 1950 (1) and to share Tim Menzies statement in 2004:

“Artificial Intelligence is no longer some bleeding technology that is hyped by its proponents and mistrusted by the mainstream. In the 21st century, AI is not necessarily amazing. Rather, it is often routine. Evidence for the routine and dependable nature of AI technology is everywhere.”

This is a fact; AI today is implemented for routine procedures in GMP environments.  As AI industrial expert, Toni is part of a committee coordinated by the AFDO, Association of Food And Drug Officials (2), which includes the FDA, which has the greatest interest in the development of Artificial intelligence in manufacturing processes.

In fact, the FDA is already building artificial intelligence into its own processes for two reasons: first, of course, for the patients, to get a safer, higher quality and more efficient product; and second, because the agency itself needs to coordinate better to cover the entire pharmaceutical industry, which extends to outsourced CMOs. They need to automate their monitoring and control process.

Interestingly, the FDA considers the application of its risk-based regulatory framework to the use of AI technologies in drug manufacturing and has recently released a discussion paper about Artificial Intelligence in Drug Manufacturing (3) identifying areas for which public feedback would be valuable.

The art of data preparation 

Never the message about the need to invest in the quality of data, even more in GMP environment, has been raised so clearly and loudly.

It is all about data, and this is the secret sauce for fruitful outcomes in using AI in manufacturing processes. There is an art in preparing the data that requires of both skilled data scientist and subject matter experts. Data need to be qualified, assesses as per its quality but also validated as per its relevance for the model to be developed.

In the end, the use of AI brings quality to the processes where it is used. Example like AI-guide process monitoring shows that a bioreactor fermentation process can be controlled with no manual interventions, supervising, and monitoring multiple relevant factors to detect basically real-time underperforming batches.

it might come a day, that bioprocesses as so perfectly controlled that final product analysis for quality control purposes might not be necessary anymore.

References

(1) By the 1950s, a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason to solve problems and make decisions, so why can’t machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

(2) About The Association Of Food And Drug Officials (AFDO). The Association of Food and Drug Officials (AFDO) is a well-recognized national organization that represents state, territorial, and local regulatory. The Association’s principal purpose is to act as the leader and a resource to state, territorial, and local regulatory agencies in developing strategies to resolve and promote public health and consumer protection related to the regulation of food, medical products, and cosmetics. afdo.org

(3) https://www.fda.gov/media/165743/download  

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I have always been fascinated by Sci Fi representation of labs. The voice of a central Artificial Intelligence can be heard in the laboratory to provide services to laboratory workers and trigger general alerts in catastrophic movies. I think there is some truth in this imagination of the laboratory of the future, but it is a safe bet that the laboratory 5.0 will not look exactly like what is imagined. Let’s investigate!

Internet of Lab Thing: What are we familiar to?

We already live today with many systems that have enabled the digitalization of the laboratory:

• Internet allows access to knowledge bases, and enables systems communication
• LIMS, ELN, and all other software and database systems make it possible to enhance data, manage them and ensure their quality and traceability
• The laboratory processes are formalized, through operational, quality and automation oriented methodologies, generally transcribed in the laboratory’s QMS
• The instruments are becoming more and more automated, from the gas chromatography machine that automatically draws chromatograms, to new automatic sample collection machines, for example

We have adopted these examples for a long time in our daily laboratory life, and we now perceive the possibility of going further in the digitalization of the laboratory.

Internet of Lab Thing: What do we already expect for tomorrow?

Today we expect a lot from artificial intelligence, in its ability to analyze data, make decisions, make complex calculations much faster than humans.

Databases and the value of their data are fueling the development of big data, data science, and machine learning to exploit data beyond everything that has been done so far.
The good old robots already widely used in industrial production are evolving towards the concept of “cobots”. We will also have to familiarize ourselves with these “collaboration robots” that are intended to work closely with humans, not separately.
As for the Internet of Things, it is already part of our daily life at home and has not yet entered the laboratory environment. This Internet of Laboratory Things (IoLT) will reveal the potential of all these technologies, in particular by allowing the interconnection of intelligent systems, sensors and machines.

Internet of Lab Thing: So, what’s next?

The complete integration of all these technologies will lead the laboratory to the next stage of its digitization. And the integration of every lab components together will be made possible thanks to the IoLT: from humans to instruments, from instruments to databases, from sensors to central AI, etc. Sensors and lab instruments will be connected to the network and able to communicate with the whole laboratory information system. Humans also, on the same level as AI, will be able to supervise, communicate, control data, sensors, instruments, etc. That’s IoLT.

Internet of Lab Thing: “Connected humans”

Human-computer interactions will be pushed to their climax with full adoption of technologies such as :

• Virtual and Augmented Reality
• Speech recognition
• Gestures control
• Biometric authentication: fingerprints, voice prints, facial recognition. Even all together for very secure systems are fully possible
• Etc.

All these technologies are key accessories connected together thanks to the central IoLT technology.
Humans can therefore also be connected remotely. This leads to few or no humans physically present in the lab, only machines will remain there, and humans will keep control of them from their chairs at home.

Human consideration in all of this

Human is replaced in performing risky, repetitive tasks, and where he can also make mistakes. Human is also replaced wherever the “machine” is more efficient than him: in tasks that require physical strength, high computational or decision-making capacity, etc. But he is not replaced in his most interesting and rewarding tasks, he remains the master and configures the machines, initiates the systems, maintains them, supervises them, and values their results. He remains present from the initiative of the existence of these systems to the exploitation of the results.
Furthermore, each man can certainly be replaced by 1 machine capable of doing 10 times what a human is capable of. But each human can then supervise / control / etc. 10 of these machines. The ROI is easy to estimate, either on its financial or human dimensions.

From Sci Fi to Reality

A centralized artificial intelligence will have access to the ubiquitous cameras in the laboratory, and to all sources of knowledge and control: the AI will be connected to the sensors installed in the building, it will also have access to all the databases, etc. It will have in the end a capacity of surveillance and control at the height of what authors of science fiction have already imagined, like the Red Queen in Resident Evil films, a lab version of HAL 9000 in 2001: A Space Odyssey to cite only these (and certainly not the most reassuring). But are people willing to accept this? This is undoubtedly a new change which
can be difficult to adopt. But what is the point of worrying if in the end there is no longer any person physically present in the laboratory.

IoLT: the key link for integration into laboratory 5.0

Humans are already all interconnected with each other and with knowledge bases thanks to their Smartphones. It is a cultural fact perfectly acquired by modern society. These intermediate systems for interconnection will be deployed in all ecosystems, and laboratories will not be spared. Interconnection systems will appear to interconnect the laboratory in its different components. Whether the laboratory equipment is natively connected or not to the lab network, they will be equipped with totipotent communication accessories. These accessories currently have a singular name: “IoT gateways”. This
name is a bit reductive in my opinion, it should probably be seen as a “Smartphone” for equipment, for databases, for central AI, for humans in the lab. These IoT Gateways are the building blocks of the IoLT, the final link that will soon allow the integration of physical, digital, and human systems. Paving the way for a synergy of systems whose potential surely exceeds us. For the time being.

A beginning

The lab 5.0 will arrive.
The IoLT will play a major role as a key link for the lab integration of systems and technologies, and it will be a revolution in the way of working and even living. Are we ready to accept this? This will again be a new challenge with regard to the changes to be adopted.
If you thought the lab’s “digital revolution” was getting closer to its objectives, I think there’s still a lot to do, with a lot of surprises to come. We may still be at the very beginning of what we call digitalization.

Connected Labs is sponsoring and exhibiting at the Paperless Lab Academy 2020 European edition and will be available for you to contact

Thomas Perraudin, Co-Founder of Connected Labs 

Thomas is an experienced entrepreneur and computer scientist, fond of technological innovation. Founder of Cerebellis, a company specialized in software development for biology and health industries. He partnered with SoftNLabs in 2019 to create a new IoLT solution: Connected Labs

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