SPONSORED CONTENT: Informatics-derived knowledge speeds innovation in speciality chemical formulation & development
The Informatics Insights team at PerkinElmer highlight how some of the company’s systems can help companies be first to market. Please download PerkinElmer's full white paper, or continue reading below.
Allied Market Research forecasts that the global speciality chemicals industry, forecast to reach $233.5 billion by 2020.1 The industry is essential to innovation in industries ranging from cosmetics and construction to food and textiles.
Manufacturers, when looking to maintain a pipeline of new and improved products that are safe and cost-effective, rely on speciality chemicals to improve the performance or function of their goods. It is a never-ending endeavour, with the goal of gaining market share by being fast – if not first – to market with a better solution.
According to IHS Markit, speciality chemical companies must focus on innovation and sustainability. to remain competitive Rapid consumption of natural resources drives the need for sustainability and innovation “is seen as the engine that determines progress in the sustainability area”.2
As value-added ingredients that give both consumer packaged goods and industrial products their competitive edge, speciality chemicals are called on to deliver better safety profiles, regulatory compliance and cost-to-performance ratios. To achieve the desired product-performance requirements, suppliers of speciality chemicals must have an intimate knowledge of the performance aspects that are responsible for the finished products’ ultimate performance.
That knowledge comes from understanding the structure-property-performance relationship (SPPR). This requires not only better experiment design and workflows, but also close collaboration among synthetic chemists and application chemists and formulators, as well as the marketers, business development personnel and company leadership involved in new product development. Communication and information sharing must be optimised for efficient, fast development of safe and innovative consumer and industrial products.
Informatics tools optimise productivity
Informatics solutions, such as electronic laboratory notebooks (ELNs) and data analytics platforms, are the tools needed to acquire such knowledge, generate efficiencies, maintain compliance and foster collaborations that drive productivity. ELNs combined with data analytics contribute to the optimisation of innovation in several ways to:
- Increase the speed of innovation
- Increase the likelihood of success
- Assure the quality of the final product
- Minimise development & commercialisation costs
The benefit of informatics tools is that the data streams (from molecules to formulations to properties to instrumental performance to subjective performance) can be easily compiled, grouped, sorted and subsequently analysed as part of the experimental process.
Informatics allow a research team to get the most out of the experimental work and obtain structure-activity relationships (SARs) or structure-property-formulation-performance relationship (SPFPRs). The informatics solutions, however, must be leveraged within an innovative experimental process.
The innovation process
While some innovation stems from serendipitous discovery, much comes from tried-and-true processes and planning. Planning starts with business leaders working with marketing and business development teams to use customer insights to guide decisions around product development.
Perhaps the competitive edge stems from a unique performance attribute, or similar performance at a better price, or performance that offers a regulatory benefit. While regulatory restrictions are often initially seen as an obstacle, companies that are first to launch products that address regulatory concerns can be rewarded with market share.
Once product demands are identified, staff scientists, managers and senior managers begin their planning to determine how discovery and development should be pursued. These decisions are based on:
- IP & existing competitor product landscape
- Chance for success or level of difficulty
- Expected timeframe for development work
- Regulatory & safety assessment
- Financial return on investment
After the green light is given to the project, a more detailed innovation planning process starts. This innovation process pathway begins with the experimental design stage, where new chemicals, novel polymers and/or novel formulations are created. Next, these new chemicals, polymers, and formulations must be characterised such that their structure and composition is verified.
After that, the performance is assessed, either by instrumental methods or subject test methods. Similar synthesis procedures, analytical measurements, and performance tests can be quickly and efficiently planned using customised experiment templates in ELNs, such as PerkinElmer’s E-Notebook and Signals Notebook solutions. These optimise innovation via:
- Better planning of research directions through ready access to additional information, such as IP and chemical databases.
- Enhanced experimental planning by using Design of Experiment and resources like chemical property prediction and toxicity prediction.
- Access for all domains (chemistry, formulations, analytical sciences) to enable simultaneous work within an experiment.
- Better collaboration between decision makers, chemists and formulators, process and production engineers and product testing experts as experiments and results are now accessible to all parties. Everyone can see the reasoning and decision making that goes into: the work plan and why, experimental steps, findings, what they mean; the thinking process, and the scientific and logical reasoning behind the plan and the science.
- Improved knowledge management, as record-keeping of results and methods allows for faster access to information, better and faster training of new team member, and faster and more rigorous compliance management.
- Allowing for knowledge development as analytical and performance data can be combined with structure information and formulation contents. Such connected data streams allow for continuous development and optimisation of SPFPRs.
What’s more, E-Notebook and Signals Notebook are searchable, such that all team members can easily find information. That data can then be analysed using PerkinElmer’s TIBCO Spotfire data analytics and visualisation platform.
Interfaced to E-Notebook or Signals Notebook, it provides direct access to and understanding of relevant data, particularly when compared to static reporting. This allows researchers to develop predictive SARs or SPFPRs that can generate new insights for home and fabric care, personal care and other speciality chemical applications.
Data analytics in targeted innovation
SPFPRs are key to understanding the consumer or industrial product’s performance, as well as predicting useful new chemical structures and product formulations that should be explored. The structure component of this relationship consists of novel chemical or polymer structures and/or formulations.
For instance, in the speciality chemical industry, polymer compositions and molecular weight variations are explored in order to obtain the right properties needed to achieve, for instance:
- Mechanical strength
- Solubility in particular solvents
- The right mechanical properties at elevated temperatures (glass transition (Tg)-related)
- The right mechanical properties under various humidity conditions
- The desired electrical conductivity
- The targeted rheology of the polymer in solution, or
- A polymer with the right biodegradability profile
In the planning stages, the variables to be explored are identified, such as monomer types, monomer ratios, polymer molecular weight, extent of branching and, potentially, the degree of chemical cross-linking. Experts can carry our experiment planning can be executed using software or manually. As a result, the large variety of polymer structures that can be made must be prioritised.
This can be done by identifying the chemicals that are predicted to have unwanted physical properties. Such a filtering process can be executed using scientific software, such as PerkinElmer’s TIBCO Spotfire data visualisation and analytics solution, to quickly analyse test results such as solubility parameters, Tg predictors and mechanical property predictors.
This automated filtering process improves the experimental design, since it removes candidates that are very likely not going to have the desired physical properties. In addition, combining TIBCO Spotfire with modeling applications can provide additional insight on prioritised candidates.
Another example would be the search for a surfactant that has the right combination of:
- Foaming behaviour in the intended application
- Ability to wet the surface for optimum coating efficiency
- Emulsification power to create stable emulsions or to remove the right stain from a dirty fabric
- Biodegradability profile, or
- Toxicity profile
Again, an experimental design is created, either by software or by experts, and then the outcome is filtered using informatics calculations to predict, for instance, properties like octanol-water partition coefficient (logP), hydrophile-lipophile balance (HLB), foaming, type-of-emulsion predictors, endocrine disruption capability, biodegradability, activated sludge affinity and toxicity.
Utilising data analytics systems like TIBCO Spotfire in the design phases optimises the discovery process, thus decreasing the innovation time as the chance for success of finding the right chemical is increased. As a result, time-to-market also decreases.
An additional benefit of the use of data analytics at the experimental design stage is that when the properties and performance tests are eventually obtained, informatics tools can be employed to correlate the chemical structures with the physical properties, their behaviour in the formulations and the ultimate performance.
As such, optimised, tailored, and proprietary predictive models can be constructed for SPPRs or, in final products, SPFPRs to be exposed. Such relationships can be utilised for predictive purposes to:
- Speed up the innovative discovery process
- Set performance-based specs for the new technology that allows for quality control targets for production
- Troubleshoot performance issues, and
- List the specifications essential to capturing underlying principles for inventions in future patents
Yet another area where data analytics can play an important role is the use of an ingredient database (commercial, public or proprietary) to gain information. This ranges from specific benefits of ingredients, such as: the odour of fragrance ingredients; regulatory restrictions, such as for surfactants or biocides; or cost information. Such databases can be used to filter the many molecule options to explore, or to weed out materials that should be used in formulations.
Data analytics in formulation management
In the speciality chemical industry or the chemical industry in general, formulation development is as critical as the ingredients, with new chemical structures being evaluated for their benefit in various consumer products. In addition to tracking the many variations’ formulations, each formulation must be cleared for safety and labelling requirements and regional registration.
Informatics tools employing databases of ingredients, excipients, commercially available raw materials and standard formulations will enable the formulators to quickly access information and make the best decisions, therefore increasing efficiency and saving evaluation time. Signals Notebook & E-Notebook allow the entire formulation process to be managed, including experimental planning and management of materials, recipes, inventory and formulation testing.
Leveraging informatics for productivity
The productivity of speciality chemical innovation can be streamlined when powerful informatics solutions are introduced. Efficient planning in the early stages can be achieved with a combination of ELN and advanced data analytics, such as PerkinElmer’s E-Notebook, Signals Notebook, and TIBCO Spotfire integrated platforms, to perform experimental design, filtering-based property prediction and analysis of high throughput methods.
This is true especially when further filtering of the experimental options is necessary because of regulatory or cost limitations. Furthermore, an informatics-based understanding of the experimental results allows for better and continuous learning of the SPPRs and SPFPRs.
These relationships, in turn, allow for improved performance-based specification settings of the product, as well as for the manufacturing process. As such, informatics-derived knowledge can lead to fewer out-of-spec batches, due to optimal quality control, plus less material waste, reduced production costs and fewer consumer complaints.
Types of speciality chemicals
Speciality chemicals can be: polymeric or non-polymeric additives used in chemical products; consumer products, such as hair and skin care, or fabric and home care goods; and industrial products from packaging materials to adhesives, inks, paints, nonwovens, and speciality coatings, or paper making and building materials.
The functionality of polymers may be rheology modifiers or thickeners, emulsion stabilisers, binders for fibres, such as paper or glass, suspending agents for particle dispersions, or surface modifiers for hair or skin conditioners and dual-use products (shampoo and conditioner in one).
Speciality polymers also include silicones that can be used to improve the strength or feel of materials, coatings for backings, such as self-adhesive stamps or labels, defoamers for detergents and other products where surfactants are used and create foam. Fiber-binding polymers are used in hair sprays and paper making to create a strong hold or paper that does not easily tear.
Non-polymeric speciality chemicals include surfactants, fragrance ingredients and food additives. The surfactants comprise those used for emulsification and emulsion stability, foaming, and cleaning or dirt removal, texture control in food and cosmetics and film-formation in coatings.