We have entered a new era where an Artificially Intelligent (AI) Laboratory today is becoming more science, than science fiction. There is a paradigm shift going on with the outdated pharmaceutical R&D blockbuster drug model which takes 10 years at a cost of up to €2.6 billion to bring a single drug to market. Big data analytics is key to transforming the future of skin disease drug development.
Skin disease is ranked as the fourth most common cause of human illness. While drug companies still chase molecules for new blockbuster drugs, millions of people are suffering from skin related diseases today. Wouldn’t it be better to find a solution that improves existing products, transforms them to deliver superior skin healing capabilities, all certified by extensive real-world, real-time, laboratory testing?
By introducing AI into a laboratory setting with an already successful business model, you take what already exists, what already works and may well have been built for another purpose, and you refine and improve it to deliver more from the same technology. It is not necessary to invent or recreate anything.
What’s wrong with the old technology?
Without automation and digitisation in a lab, researchers and technicians are wasting vast amounts of time searching for information from a variety of internal and external sources. With the ever-increasing scale of data available, it’s harder than ever to manually search through sources like:
- Laboratory journals;
- Laboratory Information Management Systems (LIMS);
- Product data sheets;
- Scientific literature;
- Patent databases; and,
- Standard Operating Procedures (SOP’s).
Digital technologies crunch through big data to convert unrealised value from diverse data sources to both keep up with innovative disruption from competitors and exceed them.
Data management improves ROI immediately with more hours saved on research and less requirement for laboratory paperwork. Software that documents, stores, analyses, shares and manages data in digital form instantly increases productivity and profitability.
Automation + AI = A Recipe for Success
AI datasets and automation are used to extract trends, patterns and anomalies from aggregated data sources. A Scinote study shows how researchers save 9 hours per week using electronic laboratory notebooks, (ELNs), while doing the same amount of work. Areas open to AI automation are:
- Quality control;
- Decision making;
- Advanced analytics;
- Predicting trends;
- Data input;
- Sharing and presenting;
Automating processes minimise the need to manually type in data, eliminates the margin of human error and saves time sharing and presenting data. Having the information accessible and available in real-time, presentable to the world with just a few clicks of a mouse allows development teams, and indeed companies, to focus on more important aspects like reducing the lab-to-market time and optimising existing processes to further reduce costs.
AI accelerates cosmetics testing and drug delivery
AI is becoming instrumental in accelerating drug discovery and bringing new drug, cosmeceutical and nutraceutical products to market. Up until now, the journey from drug discovery to establishing a new medicine on the market takes up to ten years, at a cost of €2.6 billion. Among the biggest players, the return on investment in 2016 was no more than 3.7%. Smaller companies simply cannot compete, as investment can be substantial, and the risk of failure is high.
For FDA approval of a new drug, this five-step process is mandatory:
- Discovery and development;
- Preclinical Research;
- Clinical research;
- FDA review;
- FDA post-market safety monitoring:
Implementing AI at a very early stage can find biomarkers that cause diseases. It is possible to recruit eligible clinical trial participants and sift through millions of words of text, molecules, genomic sequences, images and more in minutes. The time it takes to bring a new drug or cosmetic product to market can be reduced by many years and save hundreds of millions in funding requirements. In the case of product reformulations and combination of existing treatments, this can be reduced from years, to perhaps months.
Here’s one you made earlier, with stronger IP
Not only does AI expediate the time and lower costs for delivery of new products to the market, existing products can be enhanced and marketed in combination with existing treatments, nutraceuticals, cosmeceuticals or drugs on the back of efficient results for the input of data from skin care patients.
If you begin looking for trends, patterns and anomalies in big data, with existing knowledge of products that are already on the market, you can take trial and error out of the reformulation. By using AI to simulate contraindications on thousands of other molecules that already exist, you begin with what is known to improve on. In conjunction with a physical laboratory and human volunteers, the combination treatment solution can be confirmed with half the number of volunteers, at half the cost and in months, rather than years. That’s a saving of time and billions of dollars’ worth of trial and error investment.
AI can secure new patentable treatments with minor changes to existing products at a fraction of the cost and time. This opens the door for major savings and massive revenues, boosting existing drug and cosmetics companies’ profitability and increases ROI rapidly.
Removing animal tests for both cosmetics and pre-clinical trials
In 2013, the EU Cosmetics Directive prohibited the testing of cosmetic products and their ingredients on animal models. Additionally, the sale of any cosmetic product and/or its ingredients that had been tested on animal models was prohibited within the EU (European Commission, 2014). This regulation resulted in the need for alternative testing methods within the cosmetic and CRO industries.
Laboratory grown, full thickness, 3D human skin equivalent testing platforms are essential for use when complying with this directive. Animal models cannot accurately replicate human physiology, nor are human skin conditions like psoriasis and eczema commonly found on your average laboratory monkey.
Testing of products and a product’s claims using a life-like human skin, that creates new collagen production and shows the impact of these products on your skin’s microflora balance can be used in a wide variety of applications in other health care areas such as:
- Personal hygiene;
- Medical device;
- Wound care;
- Cosmeceutical; and,
- Drug discovery.
Hosting harmful skin bacteria which allows the introductions of toxins, bacteria, viruses and other foreign substances that trigger an immune response on a 3D human skin equivalent allows you to establish:
- A dry testing surface that offers a physical protective barrier equivalent to human skin;
- Long life of test components (Viability for 10-14 days);
- Ability to allow for harsh testing conditions and multiple applications of a compound;
- A large testing surface area (Up to 4.5cm2)
- The impact an ingredient or formulation has on skin’s microflora; and,
- The ability to show creations of pro-collagen production.
When used in conjunction with AI and state-of-the-art machine learning, it allows continuous monitoring, compares results to quality standards and provides real-time verification and client accessible reports.
Decrease Lab-to-Market time
Bringing a new drug product to market, evidently requires a phase of clinical research. A commonly used approach to find potential clinical trial participants involves hiring a recruitment firm to examine thousands of individual medical records. The cost and time involved in this process is reduced substantially when you have access to AI and machine learning.
By training a model to search by age, sex, treatment history, health status and other criteria, you can easily establish an inclusion/exclusion criterion that minimises your search to minutes, instead of weeks.
Not only is AI useful for establishing potential participants in a trial, but by recognising patterns and adapting to change millions of times faster than a human brain, it can evaluate and predict how drugs might interact with the human body. Allowing drug companies to identify potential side effects before the clinical trial starts and make corrective adjustments in advance.
What is an easy route to AI in drug and skincare product discovery?
Digitising and implementing AI in a laboratory is at the forefront of disruption in the development of products and treatments across the healthcare, nutraceuticals and cosmetics industries. Instead of only using historical data in decision making, companies now have the option of combining it with real-time and forward-looking data. Collaboration with outsourced healthcare AI experts accelerates the pace of decision making and speeds up the process of bringing new cosmetics, nutraceuticals and drugs to market. This eliminates the need to build up your own AI infrastructure and sift through raw data, both a timely and expensive process.
Traditional analytics and clinical decision-making techniques have their place, but when augmented with AI, you gain unprecedented insights into various aspects of drug development. Whether you start by automating the integration of lab reports, spreadsheets, images, text documents, or fully launch into robotics, AI ensures the cost of digitalisation is significantly outweighed by efficiency gains. Labskin AI’s 3D Human Skin technology already exists, and has been tried, tested and operational over 12 years of development by:
- Testing extremes from emulsion oils for Eczema to skin absorption of the Ebola virus;
- Producing enough artificial human skin for 38,000 experiments;
- Providing support to more than 400 research institutes;
- Demonstrating Labskin results with 30 top research institutions;
- Contributing to more than 30 scientific publications;
- Supplying 58 skincare client companies, 3 of which are in the Global Top 10;
- Data analysis and automation processes to compare datasets from multiple skin related treatments, therapies, drugs, nutritional pre and pro-biotic supplement databases against skin disease databases;
- Scientific test protocols to a control range of existing skincare products:
- Nutraceuticals that include hemp-derived CBD and Omega 3 oils;
Skincare companies use of AI and 3D Labskin provides the opportunity to:
- Simulate hundreds of thousands of combination tests;
- Take the most efficient results and test using half the normal human volunteers with the
- Labskin bacteria clone techniques that:
- Substantially reduce the time and cost of clinical trials;
- Deliver new combination treatments for multiple skin related diseases from existing drugs, nutraceuticals and cosmeceuticals; and,
- Reduce new skin treatment development from years to months.
Positive net results are that misunderstood, misused or mismanaged formulations can be transformed into new, better products or product ranges by refining, tweaking and improving the technology using Labskin AI to deliver disruptive, innovative products in months, not years, and at half the expense.
From pre-clinical drug discovery for skin diseases to skincare, healthcare, wound care, cosmeceuticals or nutraceutical product development, AI can save substantial costs along the process stages of development and deliver a faster time to market. Visit www.labskin.co.uk and then speak to our Labskin experts to learn how Labskin AI will transform your business.