iformula

By David Calvert, 19th March 2024

We have previously made the case for a structured approach to “Design for Formulation” and in this post we look at some of the important terms used in the process.

The whole process starts with a “Customer Promise”, namely what your customer expects from the product and what you promise to deliver. It may seem blindingly obvious, but we have seen in many instances that, when pressed, companies find it difficult to express in a succinct way what they are going to deliver. Developments are often driven by an internal technology development or a vague perception of what the market needs. At its best the customer promise can be expressed in one, maybe two sentences.

From the “Customer Promise”, business and technical briefs will be developed. We will say  more about those in the next article but these lead to definitions of what are the “Product Quality Attributes”. These are the properties of the final product that can be measured to determine that the product delivers on the “Customer Promise”. From these you start to define “Raw Material Attributes”, which are the properties of the raw material which are required to deliver the “Product Quality Attributes”. It is important to note that these are not necessarily measures you find on the raw material specification. As an example, the specification for the particle size of a powder may include a value for the D50, but you may find that the D10 or D90 are more relevant measures and could discuss this with your supplier.

The full list of these attributes may seem long and daunting and it is here that the “Risk Assessment” exercise is invaluable. This is not the typical risk assessment to decide whether a chemical is safe to use in the laboratory or in manufacturing but is an assessment of the consequences of what would happen to the product, and your promise, if you did not achieve the specific product or raw material attribute. Initially this assessment is based on “Prior Knowledge” (more of this in the next article) but as the process continues it is based on experimental or process data generated. By carrying out the risk assessment exercise, you develop an understanding of what is believed to be “Critical” and in your experiments you focus on quantifying this risk.

A similar exercise covers your formulation and manufacturing process and understanding the “Critical Process Parameters” which we will also cover in a future article.

In the next article, we will outline the five stages in the Design for Formulation process with a focus on the first stage “Getting Started” which is carried out before you even go into the lab. 

By David Calvert 7th March 2024

The topic of AI and systems such as ChatGPT is much discussed and opinions on the validity and value of these approaches varies widely. At iFormulate, Jim Bullock recently gave a video presentation on how AI, Machine Learning (ML) and automation are being used in formulation development and we have even investigated ChatGPT .

A recent article in ChemEurope piqued our interest further when it discussed how to measure the actual value that AI-driven chemistry labs actually deliver by establishing some common metrics. Whilst the article focusses on researchers and chemistry, we think this has significant applicability to those working in the formulation space. The target of “producing trustworthy, reproducible results that make the most of AI programs that capitalize on the large, high-quality data sets produced by self-driving labs” made us reflect on some work we carried out a few years back on open data and how this could be applied to formulations. Whilst there was enthusiasm for using and compiling formulation data from a number of sources, there was considerable concern around quality of data used, and in particular how realistic it was to expect all research to use standardised test methods. Even for something as seemingly “straightforward” as particle size measurement, how could an open data set deal with different techniques? A measure for “stability”, which is still our most common consultancy request, presents more questions than answers.

We do not wish to be viewed as holding back new technology and becoming “luddites” with regard to AI. There are clear signs that using AI for internal data which is well controlled can bring significant benefits. Croda recently published an article how they are using AI to not only improve their formulation development but also reduce carbon footprint and assess business risk. There will no doubt be other examples of applying digital technologies to formulation at the RSC event, Formulation 4.0, in July of this year.

We do believe that AI will be a game changer but as with all new technology there must be some critical evaluation and there will still be room for “non-artificial intelligence” in formulation! 

By David Calvert 6th March 2024

At iFormulate, we have long been proponents of a structured, yet flexible, approach to formulation which we have commonly called the “Design for Formulation” approach. This has been embraced by many of our clients and has formed a part of a number of customised training and workshop sessions. During these projects, we have taken clients through the approach with specific reference to their products (frequently with a Non-Disclosure Agreement in place).

Over the next few weeks, we thought it would be of value to go through the process and its different stages. Before delving into these, it is important to make the case for a structured process in the first place and that is the purpose of this first article.

In many mature organisations the development of formulations has taken an organic, evolutionary approach and often when you step back and take an objective view of the process the phrase “if I were you, I wouldn’t start there” can come to mind. Whilst this can lead to a successful development, there can often be issues such as:

·      A late product launch
·      High production costs
·      An unreliable process
·      Batch failures due to formulation not being robust
·      Inability to change manufacturing site or equipment
·      No flexibility in raw material suppliers
·      Loss of expertise and experience  

Whilst there is no guarantee that our preferred development process will eliminate all of these issues, it will minimise the risk of these occurring.

When outlining the process, we aim to remove some of the mystique and perceived bureaucracy of a well-structured approach. It cannot be denied that knowing what you did, why you did it and what the results were is not valuable and can be of significant benefit when starting new projects. Even with developments such as artificial intelligence (AI), there will always be the need for experience and subjecting decisions to some friendly scrutiny.

Through the next few weeks, we will expand on how to maximise benefits from the “Design for Formulation” approach and the next article will look at some of the terminology and how these help to drive the process  

The publication is Open Source (CC-BY 4.0) and can be read and downloaded at the ACS website or viewed below.

Following her M.Chem, which included the experimental project supported by iFormulate, Ana has gone on to complete a PhD at the University of Nottingham.

January 2024

With Professor Steven Abbott

At iFormulate we know that formulation challenges are rarely simple, requiring multiple pieces of science to be brought together. But formulators cannot know every key principle or be expected to do the calculations behind those principles.

So we are delighted that Professor Steven Abbott – a good friend of iFormulate – has produced for the formulation community an extensive “Formulation Scientist’s Toolkit” containing the tools, and links to his many apps that bring the tools to life. Best of all it’s a free and open-source PDF that you can simply download here. Any time you have a problem, you open it and find the tools you need, along with explanations, equations and links to the apps. At the top of this article you can view a video of Steven explaining the Toolkit.

How do you find what you want? The book is built on a matrix of Products and Sciences. You find a Product that’s relevant to your needs, and it contains links to the different Sciences you need to formulate in that space. Click on any of the Science links and you see other Products for which it’s relevant. Now you can get formulation inspiration from other domains. That’s one way to use the FST. You can also use the PDF Search function, or use the Thumbnail bar or Bookmark bar, whatever gets you to the information you need.

Some will use it to find the thing they need right now. Others will choose to get lost in the interconnections between Products and Sciences. Some will spend a lot of time exploring apps. It’s a toolkit, not a book. And it’s your toolkit so you can use it any way you like.

Having taken a look ourselves we think that you will enjoy re-discovering things you’ve forgotten you knew, and learning things you didn’t know that you didn’t know! If the science gets heavy going, Steven lightens things with stories from his own formulation life, including learning from his mistakes.

As Steven freely admits, the FST is imperfect. But it’s been designed to be easy to update. Anyone with a complaint, correction, suggestion can email him and things will be fixed. Steven has also asked our help to identify what should be priorities for new Product and Science chapters, so we welcome your views. As he said to us recently “The more the community helps the FST to grow, the more tools we have for creating better formulations, faster.”

Happy exploring, and it will be great to hear your feedback – please let us know what you think.

iFormulate’s Dr Jim Bullock interviewed for Azo Materials at ChemUK 2023

This year, we were once again pleased to be attending and speaking at ChemUK at the NEC in Birmingham. The event has rapidly established itself as being the premier meeting point for the UK’s chemical industry and its supply chain. This time, I had the pleasure of being interviewed by Skyla Baily of Azo Materials and was able to explain a little about how we work to help our clients with their challenges in formulation. You can find the full interview here.

ChemUK returns to the NEC on 15th and 16th May 2024.

By Dr Jim Bullock, iFormulate Ltd.

For the full article, see  AgroPages 2023 Formulation & Adjuvant Technology magazine and website pages.

In recent years, much attention has been paid to the environmental problems caused by plastic pollution and in particular, by microplastics – small pieces of plastics which may be found in the environment and in the food chain. The EU defines microplastics as being:

″Particles containing solid polymer, to which additives or other substances may have been added, and where at least 1% w/w of particles have all dimensions between 0.1µm and 5mm, or for fibres, a length of between 0.3µm and 15mm and a length to diameter ratio of more than 3.″

After a period of fact-finding and consultation lasting several years, the European Commission published a draft Regulation (amendment to REACH) in 2022. This would place restrictions on microplastics intentionally added at 0.01% or more by weight. Excluded from the restrictions would be natural polymers, biodegradable polymers and polymers with at least 2 g/l solubility in water. ECHA published the draft amendment to the REACH Regulation in August 2022. The proposal is going to the EU Parliament, and it is expected that there will be a transitional period of five years after entry into force for uses of agricultural and horticultural products.

Although much of the microplastic contamination we see in the environment has its origin in packaging materials and car tyres, product formulation plays a role too. These restrictions will be especially relevant for cosmetics, household products and agrochemicals. However, with the elimination of plastic microbeads from cosmetics, the spotlight has moved onto other industries and application areas. So first, let’s take a look at the challenges for formulators of agrochemicals who may be tasked with removing many of the polymer materials used in products such as microencapsulated formulations, seed treatments and slow-release fertilisers…

To read the rest of this article go to AgroPages 2023 Formulation & Adjuvant Technology.

By Dr David Calvert, April 2023

Schoolchildren are often the trend setters with regard to consumer goods and I recently came across a product called “air-up” which is causing quite a stir. The claim is that you simply drink water but that the brain is tricked into thinking that the water is actually flavoured, thanks to the addition of aroma pods such as passionfruit, elderflower, coffee, cucumber and cola. Taste has always been a complicated matter and we know that sight, smell as well as the actual taste-buds can play a role in what the brain registers.

The formulation science behind this product appears sound and it got me round to thinking about how commonly the success of products relies upon appealing to the human senses and how this can be difficult to measure. With food and drink we all can see how the human sense plays a key role and how despite much effort there is still a strong reliance on consumer panel and expert tasters from products such as wine, coffee and tea.

There are other areas where “experts” are needed, however, and I think back to my time working in the pigments industry where colour matchers were still the best source of information that even the very best spectrophotometers could not provide. Moving further down the supply chain, if we think of almost any coatings product, then colour and how people react to the colour and even the name can be critical to the success of a coating. Have you been seduced by “Flamingo Flock”, Frosted Papaya” or “Proud Peacock” from the Dulux Trade Pro Colour Guide, or if cars are of more interest, what about “Shooting Star Grey Matte” or “Orinoco” or “Fjord”?

In the pharmaceutical area, the efficacy of a product can clearly be measured by how the patient feels and the inclusion of placebos in medical trials tries to remove the “feeling” element from results. Moving into healthcare and personal care, creams are often marketed as having a soothing effect and how many times have we all benefitted from lotions and creams both before and after we venture onto those sunbeds while on vacation.

I could go on with many more examples but I think the previous examples make the point that the senses cannot be ignored by any formulator and often a formulator should use their own senses as an initial filter before getting to convoluted data management techniques.

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By Dr Jim Bullock, April 2023

There has been a lot of recent debate about the opportunities and pitfalls of using artificial intelligence (AI) and a lot of discussion about the possible impact on journalism, literature and the arts. Rather less of the debate has been about its impact on science and technology. To see whether the popular generic AI tools could be of use and interest, I had a play with the (in)famous ChatGPT and asked it a question of the sort a typical formulator might ask. I decided to ask ChatGPT to write a detailed recipe for the manufacture of an oil dispersion (OD) formulation containing the herbicide nicosulfuron as the active ingredient. It’s a tricky question but not an unreasonable one, as several agrochemical companies do produce formulations of this type based on that particular active ingredient. Below you can see the response I got. Let’s see what it got right, what it left out and what looks decidedly odd.

First of all, it has chosen some reasonable ingredients, but although I asked for a “detailed” recipe, the list is a bit generic. Including a mineral oil solvent is reasonable and OD formulations do typically include at least one surfactant to act as a dispersing agent in the oil phase as well as a second surfactant to emulsify the oil phase when diluted in water in the spray tank. But as a formulator I have to choose from many hundreds of possible candidate surfactants and this recipe doesn’t really tell me where to start, nor does it suggest what amounts of ingredients to use. I’d also be worried about the tendency of dispersions to settle out, and I’d ask myself whether a rheology modifier might be needed in this case.

Moving on to the equipment, again the list is a bit generic. Typically, the particles of active ingredient have to be ground down to a few µm in size, in order to provide a stable dispersion formulation. The recipe doesn’t include any milling equipment which is surprising. Also surprising is the use of a centrifuge to “homogenize” the formulation. Correct me if I am wrong, but centrifuges are usually used to separate particles from liquids, the opposite of homogenizing them. Then there’s the use of a pH meter. Although ChatGPT has correctly worked out that this is an oil-based formulation, I think that formulators would struggle to get a meaningful pH measurement on a formulation with a mineral oil as the solvent.

So, what have we got? Well, although we can recognise a few reasonable points, it looks a bit like the results from multiple search engine results have been put in a blender and homogenized (and not with a centrifuge) to give us something that we really couldn’t make a start with. A real-life formulator would make proper use of context (for instance “what do we know about formulations of other active ingredients with similar physical properties to this one?”) and sources of both online and offline information to come up with a starting formulation to test. As well as interrogating the scientific literature and patent databases, a formulator might pick up their old-fashioned phone and call some suppliers of co-formulants for recommended surfactants and other ingredients.

So does this mean that AI is of no use to formulators? Well in fact AI is becoming established as a useful formulation design tool, it’s just that successful AI systems can make proper use of context and user data to achieve the desired results. And AI can be combined with automatic high throughput screening and statistical methods. I don’t want to single out any particular company or organisation, but a glance at the speaker list for last years’ SCI event “AI/ML in Formulation Design” will lead the reader to some interesting examples.

Finally, I gave ChatGPT one last chance to redeem itself. As an enthusiast for both science and the arts, I thought I’d ask it to write a Shakespearean sonnet that describes how to formulate a biopesticide. To my non-literary eye, what came back might not be much use as a formulation recipe but it’s quite poetic…well, nearly. All’s well that ends well, I suppose.

 

ChatGPT response (don’t try this at home)

Oil Dispersion (OD) formulation containing Nicosulfuron herbicide as the active ingredient:


Ingredients:

Nicosulfuron (Active ingredient)

Solvent (E.g., Mineral Oil)

Surfactant

Emulsifying agents

Antioxidants


Equipment:

Mixing vessel

Stirrer

Measuring beakers or flasks

pH meter

Centrifuge


Instructions:

Prepare the solvent by heating it to 40°C to reduce its viscosity.

In the mixing vessel, add the required amount of surfactant and emulsifying agents.

Slowly add the heated solvent to the surfactant and emulsifying agents mixture, stirring continuously.

Add the required amount of nicosulfuron to the mixture and continue to stir.

Adjust the pH of the mixture to 7.0 using a pH meter.

Mix the formulation for 30 minutes to ensure that the ingredients are well dispersed.

Allow the formulation to cool to room temperature.

If required, homogenize the formulation using a centrifuge.

Add the required amount of antioxidants to the formulation and mix well.

The OD formulation is now ready for use. Store it in a cool, dry place, and shake well before use.

Note: The above recipe is a general guideline, and the exact proportions of the ingredients may vary depending on the specific application requirements.

iFormulate was once again delighted to be able to support the School of Chemistry at the University of Lincoln as the latest graduates from the University’s M.Chem course (completing in summer 2022) celebrated their achievements in January 2023.

This year’s winner of the Professional Practice in Chemistry prize was new M.Chem graduate Aaliyah Hart. The award was sponsored by iFormulate. Congratulations are also due to Aaliyah for securing a position as a scientist working for LGC Assure after completing her studies at Lincoln.

Since the inception of the Lincoln M.Chem course, iFormulate‘s David Calvert and Jim Bullock have and presented a short lecture series on the industrial applications of Formulation Science and Technology to Lincoln’s second-year M.Chem students.