Fundamentals of Solid Formulation

Henry Morrison is an expert in the field of solid state chemistry and has been working in the pharmaceutical industry for over 20 years.  He obtained his undergraduate degree at Old Dominion University and his graduate degree at The University of Tennessee, where his thesis work focused on the study of polymorphism of lanthanide and actinide tri-chlorides.  He began his career at SSCI (West Lafayette, IN) where he continued to pursue his interest in API small molecule polymorphism and salt selection.  He later worked at Merck (Rahway, NJ) in the analytical physical measurements group, followed by Amgen (Thousand Oaks, CA) as a preformulation chemist.  He currently works in the process chemistry department at Gilead (Foster City, CA) where he supports API crystallization design, mapping of bulk properties, and furthering the understanding of API physical phases.

Presented is an approach for correlating active pharmaceutical ingredient (API) bulk properties such as particle size, surface area, powder flow, morphology and dissolution in order to provide an understanding of their collective impact on the formulation and manufacturing of drug product.  This methodology, termed the API Camera, shows the significance of understanding the relationship of the various bulk properties to each other rather than monitoring them independently to draw conclusions.  Three case studies are provided, of which two show how the API Camera was utilized to troubleshoot issues that arose during drug product manufacturing, while the third focuses on how the methodology was proactively used during process development to guide decisions for an early stage program.

Q1. Can these models be applied to a semi-solid formulation intermediate, e.g., in a melt spray congeal process where the API is distributed in a molten lipid and then congealed into spherical beads?

The models can be built based on the data collected on the resulting spherical beads. A heptane bases PSD method should work for a wet method, or a dry method could be used. Krypton based surface area could be used on the beads. Finally, ring shear flow instruments sell several sized sample cells to accommodate diversity in samples tested.

Q2. Bulk properties and API properties are directly related to the particle attributes notably morphology. Do you have an example in which you have used this information to tailor process parameters to obtain a desired morphology?

Per case study 3 discussion, as process DOEs or early RnD development investigations are conducted (including PAR and alt solvent ratios, stir speeds, etc) the API camera can be used to map the resulting affects on the yielded API. The data can then be used as road maps to target the quality attributes that may be of interest to support crystal engineering. Also please see my 2015 publication which touches base on this topic: Morrison et al, Org. Process Res. Dev., 2015, 19 (9), pp 1076–1081 (Correlation of Drug Substance Particle Size Distribution with Other Bulk Properties to Predict Critical Quality Attributes)

Q3. Morphology can be affected by impurities. Have you checked the purity of the API?

Yes, as programs progress though development, purity is monitored during RnD development and reported in the certificate of analysis for GMP lots.

Q4. What is VMD?

Volume mean diameter (AKA volume weighted mean, AKA D[4,3]) is the mean of a particle size distribution weighted by the volume. It is the mean diameter, which is directly obtained in particle size measurements, where the measured signal is proportional to the volume of the particles. It provided insight into the center of gravity for a distribution that is multi-modal.

Q5. Did you characterize the Bulk density of API and its impact on flowability of the powder?

Bulk and Tap density data was collected as well as the Carr index calculated. I have tried to model these parameters to PSD and SA; however, to date I have been unable to find a successful correlation between these parameters.

Q6. Any way to troubleshoot problems related to static?

On large scale batches (specifically milled lots) this can be a problem and attributes to the cohesiveness of a powder. Unfortunately I don’t have much experience with this phenomena outside of troubleshooting small powder aliquots with de-static devices.

Q7. For one case we observed that larger PSD (D90) of API can reduce the dissolution variation of tablets and increase the dissolution. Do you have any thoughts on that?

I would suspect that it might be related to cohesiveness of the API having smaller PSD values. My suggestion would be to correlate the PSD to flowability of the lots in question to see if there are differences that can be further correlated to the dissolution observations.

Q8.Slide 11: What was the drug load in the formulation in case study 1?

~30%

Q9. How applicable is the API camera to DP?

The principles of the methodology could be applied to dry blends for insight and understanding

Q10. The Morphologi G3 dispersion unit can cause breakage of acicular crystals due to the high air flow and impact of crystals on the unit. This can have a large effect on PSD and aspect ratio measurements. Did you investigate this, and would you recommend manual dispersion of crystals for analysis?

For the purposes of these investigations, we hand prepped our samples on microscope slides by dispersing the API in oil and using the microscope slide stage on the G3.

Q11. What was the drug loading for the tablet formulations in these case studies? Could the limitations of the API bulk properties be addressed using formulation and/or process fixes?

Study 1 = ~30%, Study 2 = ~20%, Study 3 = ~10%. For high drug load DP, sometimes it can be challenging to overcome the properties of the API via the formulation process. But yes, it is possible if you can formulate with appropriate glidants (however, you are limited by how much glidant can be used). As mentioned in case study 3, the models presented can act as a road map to target quality attributes based on the process experience; therefore, there are opportunities to overcome issues via process optimization for a program need.

Q12. What are some common techniques to improve flow problems?

1) grow larger particles 

2) attempt to change morphology via crystal engineering 

3) appropriate glidant in the DP blends

Q13. Can you comment on the difference and advantage of your mini-dissolution method for API and the more commonly used intrinsic dissolution method?

Both can be used and are equality valuable. Intrinsic dissolution requires manipulation of the API into a disk surface, therefore you can potentially loose insight that you might gain from allowing the free-flowing powder to distribute within the dissolution media which is often corelated to the cohesiveness of the powder itself.

Q14. Can you comment on ring shear flow measurement vs. simpler methods such as angle of repose or Hausner ratio?

I have not personally investigated the angle of repose method before, but I certainly understand that Ring Sear Flow instruments may not be commonly available, therefore I think it merits investigations to see if API Camera flow models can be built using this. I have attempted to model the bulk properties I spoke about with Bulk and Tap density data (and there for the Hausner ratio); however, to date I have been unable to find a successful correlation between these parameters via modeling.

Q15. The case studies showed the tripods are working collectively, so do we need to set API specification that includes PSD, surface area and powder flow?

My recommendation would be No. These tests should be measured as For Information Only (development tests). The models that can be generated via the API Camera can then be utilized to optimize a process so that teams can consistently target the quality attributes identified to support successful DP manufacturing.

Q16. Did you try using calculated particle surface area instead of BET surface area? Could that be an alternative?

Most PSD instruments can calculate a surface area; however, since the laser diffraction units are blind to morphology, the data is useless as applied to the API Camera models. Image based PSD units can attempt to do the same thing, but again, the data is based on 2D images and can provide not context on surface texture or porosity.

Q17. Can you please suggest the modeling software?

Machine learning methods, statistics, and data clustering were performed using Python 3.9.0 (Python Software Foundation), employing the statsmodels v0.12.2 library and scikit-learn 0.24.1 library.

Q18. How to work around for moisture sensitive APIs?

You can work around moisture sensitive APIs using lower moisture/less hygroscopic excipients, manufacturing in low relative humidity-controlled rooms, storage of intermediate products (e.g., preblends, final blend, bulk tablets) under dry conditions, and decreasing environmental moisture availability through packaging.

Q19. Were contact angle measurements or surface measurements done?

At the time of these investigation, a contact angle instrument was not available. We have since on boarded one and are actively attempting to evolve the API camera methodology by adding this technique as an added accessory.

Q20. While modifying bulk properties needed for DP, have you seen changes in dissolution rate of the API?

Yes, morphology differences can significantly affect dissolution when comparing samples of the same PSD but different shapes (again SA is then a better surrogate to predict dissolution). Also, as discussed in the presentation, PSD distribution shapes and their relationship to their resulting surface area will certainly affect dissolution.

Q21. Have you used this API camera model to predict flow ability of micro particle suspension? Any insights that you can share?

The models have been used to map and predict the flowability of the micro particles used to generate the suspensions, but not the suspensions themselves. We have recently used the API camera to trouble shoot differences in resulting API lots coming from 2 different jet mills to show that although both mills provided lots of the same PSD, the lots have very different cohesiveness (and wettability via our new contact angle instrument) as a function of the milled used. The data therefore explained why Mill A lots formed pastes when formulated into suspensions, while Mill B lots formed injectable suspensions.

Q22. To improve the bioavailability particle size reduction is preferable, or crystal modification like salt or cocrystal?

Both should be investigated. Typically, a free base/acid is investigated in early development and bioavailability challenges can be overcome as a function of particle size reduction. However, this can buy you only so much of an improvement that if you still do not hit the targeted exposure of interest, crystal modification should be investigated. At which point you can again apply particle size reeducation to the new salt phase should it be needed.

Q23. How do we calculate VMW?

Volume weighted mean is calculated by the instrumental software and is reported by its alternate name of D[4,3].

Q24. For the G3 Morphologi, if the API is too cohesive, can you disperse the primary particles to avoid measuring the morphology of agglomerate?

Samples can be prepared on microscope slides by dispersing the API in oil and characterized using the microscope slide stage on the G3. Having said that, true agglomerates represent a bulk property and should be accounted for to understand lot to lot differences.

Q25. Please discuss the model's ability to take into account the probability of polymorphic interconversion during milling, blending, granulation, and compression steps.

The models will not provide insight into the probability of polymorphic conversions, but the data can be correlated to the affect these conversions have on the bulk properties. Behind the scenes of the API camera, XRPD, DSC, TGA, etc are utilized to characterize the various API batches, and when polymorphic conversions occur, the resulting bulk property data points can often show up as an "orange in the basket of apples" when the curves are built on lots that are based on Form I and then a data point of Form II is modeled within the Form I curves.

Q26. Please discuss the model's ability to select among the following tableting methods: dry granulation, wet granulation, and direct compression.

The models will not necessarily drive the decision for selecting the tablet making process, but rather determine if the API properties are amicable to the various routes.

Q27. How would the model benefit from a better first-principles characterization of the API crystal structure?

It's possible that density functional theory could give insight to possible morphological possibilities of the crystal and potentially their surface related attributes, but at this time I believe this is a yet unexplored space worth looking into.

Sudhakar Garad received his Ph.D. from the Bombay College of Pharmacy, University of Mumbai, India in 1998 in Pharmaceutical Drug Delivery Technology with Dr, M.S Nagarsenker. Soon after completion of his Ph.D., he worked with Pfizer, India for less than a year in clinical research. Taking into consideration his excellent credentials during his Ph.D, he was selected as a post-doctoral fellow at the University of Connecticut to understand micro-environmental properties above dissolving surface of the polymers with Dr. Robin Bogner. After his post-doctoral fellowship, he worked with Vertex pharmaceutical for a period of three years as Senior Investigator in formulation development group. After Vertex, he joined Novartis as a Group Head. He worked with Novartis for 9 years and joined as a Director of formulation development at Cubist Pharmaceuticals/Merck for 2.5 years. Currently Sudhakar is working as a Global head of Chemical and Pharmaceutical profiling (Discovery Pharmaceutics/Novel Drug Delivery) and Technical R and D, Disease Area Head (Hepatology, Nephrology and Hematology) at Novartis. His primary role is collaborating with research colleagues and build the right biopharmaceutical properties into new chemical entities, if not enable them via solubility/dissolution enhancement technologies to expedite molecules into tox and clinical studies. He is also responsible for enabling research, clinical and commercial molecules via novel delivery technologies (Brain targeting, permeability enhancement, TI improvement etc). He has many publications, book chapters and patents. In last 20 years of his career he has taken more than 200 new chemical entities in clinical studies across dozen disease areas (CVM, Oncology, ATI, MS, Respiratory, NS, GI, Antibacterial, Tropical diseases, HCV etc), via oral, parenteral, inhalation and transdermal route of administration.  

Solid form in a Dosage form (formulation) plays a very important role in preclinical and clinical studies. In many disease areas, most of the new chemical entities have a poor aqueous solubility. These are often available in the amorphous and chemically impure form for in-vitro enzyme assay, pharmacokinetic, pharmacological and toxicity studies in animal models. These molecules are often dosed as a suspension/solution and candidate selection is done based on results obtain during these studies. It is very important to select a physical form as early as possible so that same form can be used for preclinical studies and clinical studies. The change in physical form or formulation principle could significantly affect the bioavailability and stability of formulation. Selected candidate must be physically and chemically stable during formulation development process with desirable pharmacokinetic parameters. If bioavailability is significantly different than that obtained from preclinical studies, lot of efforts will be required in formulation development. Ultimately it will increase the time to develop formulation for first in human. This presentation will cover the different technologies for the success of new chemical entity from preformulation to drug delivery into clinic using right solid form.

Q1. On slide7 - what are the % representing per each class?

Percentage represents the number of new chemical entries in each BCS class. BCS II is highest percentage (less soluble).

Q2. What are the strategic approaches that your discovery team is using to improve solubility?

1.Adding solubilizing group to molecule, 2. Having pKa functionality, so salts can be made 3. Stick less than 500 MW if possible, if MW is more than 700-1100 range, have log P more than 5, so that lipid based delivery can be used. If all cannot be done, have supersaturation solubility more than 0.1 mg/mL so that amorphous dispersion or nano technology can be used.

Q3. How do you test for solubility/dissolution differences across pediatric/infant populations since the pH and bile salt concentration is different than in adults especially with Solid dispersions and API salt forms which could lead to crashing because of the common ion effect?

The goal of formulator is to keep molecule in solution above pH 5, so it covers pediatric, adults and geriatrics.

Q4. How do pKA values help to decide salt formulation?

Molecule ionizes depending on pKa. Take an example of a molecule with pKa 4 and which is acidic, which means at pH 6 it will be completely ionized. Based on Henderson equation, ionized molecules are soluble. We take advantage of pKa and make salt. For Parenteral we need pH 6-8, at that pH molecule is highly soluble. So, this is how we take advantage of pKa to make salt and formulate a solution for parenteral molecule. Just an example, same for other dosage form too.

Luca Iuzzolino works as a Senior Scientist in computational chemistry. In this role, he provides expertise in solid state modeling and is involved in a variety of drug development projects in close partnership with experimental collaborators. Prior to joining his current company in 2019, Luca received his PhD at University College London (UCL) under the supervision of Sarah L. Price, with a thesis titled ‘Expanding crystal structure prediction to larger and more flexible molecules of pharmaceutical interest'. He has recently relocated from Switzerland to Rahway, NJ, USA.

Different solid forms of the same molecule can display remarkably different properties, including physiochemical stability, solubility and mechanical behavior. One fundamental component of small-molecule drug development is the comprehensive screening and characterization of the crystalline forms (or polymorphs) of the active pharmaceutical ingredient (API), as well as its solvated and hydrated forms. When the pure API cannot form crystal structures with suitable properties (e.g. sufficient solubility), alternative solid forms, e.g. salts or cocrystals, are explored to strike an optimal balance. The solid form chosen for the formulation of final pharmaceutical product must possess an optimal set of attributes and retain them throughout the whole manufacturing, storage and delivery lifecycle. However, experimental solid form screens are costly and laborious and are based on a trial and error approach that does not necessarily guarantee that all the relevant solid forms are identified. In turn, this is not the most efficient use of time and resources while leaving the door open for late-appearing, more stable solid forms. Solid-state modeling can be of tremendous value to drug development, as it can help right-size the experimental effort, assess the presence or absence of risks associated with the chosen form and direct experimental efforts towards the most relevant threats and opportunities. In this presentation, we show how solid-state modeling, including crystal structure prediction and virtual cocrystal screening, is employed at our company to support drug development projects, and how its integration with experimental efforts has made it a vital component in the solid form selection process.

Q1. Slide 21: Why do we see the differences between the calculated and observed powder XRD? Is it related to peak shape, or to the structural model itself?

The structural model is never perfect to start with, and the PXRD pattern can have some weaknesses (e.g. preferred orientation). Furthermore, the CSP-generated structures are generally optimized at 0 K while the PXRD pattern is collected at finite temperature. Hence, although refinement modifies the model to minimize the differences between the calculated and the observed PXRD patterns, some differences will always remain.

Q2. Can we use electron diffraction to help CSP? If so, how?

There are examples of electron diffraction been used in combination with CSP to solve crystal structures (e.g. Eddleston, M.D., Hejczyk, K.E., Bithell, E.G., Day, G.M. and Jones, W. (2013), Determination of the Crystal Structure of a New Polymorph of Theophylline. Chem. Eur. J., 19: 7883-7888. https://doi.org/10.1002/chem.201204369), as well as for full structure solution (Christopher G. Jones, Michael W. Martynowycz, Johan Hattne, Tyler J. Fulton, Brian M. Stoltz, Jose A. Rodriguez, Hosea M. Nelson, and Tamir Gonen ACS Central Science 2018 4 (11), 1587-1592 DOI: 10.1021/acscentsci.8b007600). Hence its application in conjunction with CSP to elucidate crystal structures is possible. Furthermore, its ability to solve crystal structures to be compared against CSP-generated data can enhance a computed crystal energy landscape.

Q3. Do you do calculations of Fvib for all, or a subset of predicted structures? If a subset, what is your cutoff, given that re-ranking of structures can be very significant compared to the 0K results?

Fvib calculations are very computationally expensive, so they are always limited to a subset of predicted structures. Furthermore, given this expense, I use no pre-defined lattice energy cut-off, and the choice of how many to consider for Fvib calculations will depend on various considerations: nature of the crystal energy landscape, where the matches to known forms are located, complexity of the compound itself, importance of the program and amount of computational resources available.

Q4. How do you separate solid solution, eutectic and co-crystal based on a DSC curve?

To my knowledge is not possible to definitively separate them by DSC only, and further measurements are required. 

Q5. How much time is typically required for crystal structure prediction?

This will strongly depend on available resources and on the software used. As a rough estimate, from my experience, a complete CSP study aimed at fully de-risking the lead form of a typical small molecule drug candidate can often take weeks to months on a few hundred CPUs. For CSP studies more limited in scope (e.g. a targeted CSP for structure solution), it can take significantly less, down to a few days.

Q6. Can COSMOtherm predict cocrystals in the presence of other solvents?

The virtual cocrystal screening tool in COSMOtherm does not currently allow to account for the role of solvents on the formation of cocrystals for some given coformers.

Q7. What is the reference "(13)" on the COSMOtherm slide?

Loschen, C.; Klamt, A., New Developments in Prediction of Solid-State Solubility and Cocrystallization Using COSMO-RS Theory. 2016; pp 211-233.

Q8. Solving crystal structures from PXRD data is also possible using ab initio powder structure analysis i.e., DASH. What is the advantage of using CSP to solve the structure from PXRD data?

Crystal structure solution directly from PXRD data requires indexing. However, PXRD patterns cannot always be indexed effectively and confidently (e.g. because of the presence of phase mixtures). In these situations, CSP-generated models can be the only viable starting points for structure solution. Furthermore, using a starting model from CSP ensures it is a true and competitive energy minimum, increasing the confidence in the solved crystal structure.

Q9. Does COSMOtherm software provide the most favorable API-coformer ratio?

It is possible to consider different API-coformer ratios and compare the resulting enthalpies of mixing. However, since we are mostly interested in prioritizing promising coformers for experimental screening, calculations limited to 1:1 have been found sufficient for this purpose, even when the experimental cocrystals turn out to have different stoichiometric ratios.

Q10. How was the entropy of different crystal forms calculated to determine their stability at room temperature?

From the phonon density of state calculated on the CSP-generated crystal structures with the harmonic approximation. More details can be found in Hoja, J.; Ko, H.-Y.; Neumann, M. A.; Car, R.; DiStasio, R. A., Jr.; Tkatchenko, A., Reliable and Practical Computational Prediction of Molecular Crystal Polymorphs. eprint arXiv:1803.07503 2018, arXiv:1803.07503.

Q11. Is it possible to accurately compute solubility of different polymorphs?

The relative solubility ratios of different polymorphs can be easily calculated from the free energy differences at a given temperature (for the formula, see Mortazavi, M., Hoja, J., Aerts, L. et al. Computational polymorph screening reveals late-appearing and poorly-soluble form of rotigotine. Commun Chem 2, 70 (2019). https://doi.org/10.1038/s42004-019-0171-y). The absolute solubilities are much more difficult to compute as they will also depend on the energy of solvation of the compound in a specific solvent system.

Daniel Joseph Price is strategic marketing manager for MilliporeSigma's portfolio of solubility enhancement and sustained release excipients. In addition to portfolio responsibilities, Daniel has a leading role in several key innovation initiatives, including 3D tablet printing and computation formulation development. Prior to his current role, Daniel was a Marie Curie Fello, also at MilliporeSigma, with a focus on amorphous formulations. Specifically, Daniel's expertise lies in mesoporous silica and thermodynamics of amorphous formulations. Daniel has a background in medicinal chemistry and formulation, and studied towards his PhD in pharmaceutical technology at Frankfurt Goethe University.

Poor aqueous solubility of small molecule APIs is a major challenge in the development of modern day oral solid dosage forms. One approach is to enhance API solubility with amorphous formulation as a solid dispersion. Historically, polymeric solid dispersions have been the technology of choice. However, this approach may have some downfalls when considering the ability to stabilize compounds in the amorphous form, especially poor glass former compounds with high propensity to re-crystallize. This seminar will examine amorphous stability from a theoretical perspective in the context of polymeric solid dispersions and mesoporous silica formulations. Finally, recent data demonstrating the potential of mesoporous silica for superior amorphous stabilization of poor glass formers will be presented.

Q1. What do you think about co-amorphous for stabilizing amorphous phase?

A very interesting topic, but still relatively immature as a technology. Several studies have show that amino acids are good candidates for this approach, and also small molecule co-formers such as sorbitol and meglumine. The one question to address is the overall stability of such poor glass formers in this approach- as far as I am aware this has not yet been looked in to.

Q2. What is the maximum drug load for the Mesoporous Silica formulations?

30-50%

Q3.Notice only HME was used for comparison. Do you know of any experiments during SDD formulation that have been done to show whether phase separation or recrystallization also occurs?

HME was selected as a model formulation in this study, but work is ongoing to also assess spray-dried dispersions. The hypothesis is the same, that silica will stabilize poor glass formers to a greater extent than the polymeric SDD.

Q4. What is the maximum loading efficiency for mesoporous silica and how will the crystallization propensity of the drug drive that?

30-50%. For poor glass formers, a loading of 50% is not impossible, but there is an inherent risk due to the high crystallization tendency.

Crystallography has been Luca's main focus throughout his scientific career, starting from his PhD in Newcastle with Bill Clegg, on the supramolecular chemistry of polycarboxilic acids. During his PhD he was also part of the EPSRC National Crystallographic Service team, entailing regular use of synchrotron facilities, first at Daresbury then at Diamond.

After a postdoc in Kari Rissanen's group in Jyvaskyla and back in Newcastle later on, Luca moved into the commercial sector as an XRD application scientist for Rigaku, at the time headquartered in Berlin, were he broadened his view on the plethora of interests and applications revolving around XRD worldwide. He then joined GSK in Stevenage as crystallography SME, overlooking late stage development assets within a MatSci group. There he is currently managing the SC-XRD facility and providing structural insight to physical properties of crystalline forms in development. Luca is also vice-chair of the BCA industrial group.

I will present some examples from my daily tasks at GSK where crystallography has been used in combination with molecular modelling to address challenges in structure determination, as well as in solid form properties understanding and risk management. Moving on from these examples I will provide an applications perspective on how CSP and modelling more in general can further benefit solid form selection and risk management in pharmaceutical development.

Q1. How do you calculate mechanical properties? Specifically, what is more important, a molecular level understanding or a particle level understanding?

The values reported in my presentation for Young’s modulus and bulk modulus were obtained in CASTEP and are based on DFT modelling, based on a simulated strain of 1%. On the second, I would say they are both equally important: in an ideal world, particle properties should be determined once you have a correct molecular-level understanding, though things like defects can complicate the picture…

Q2. Are mechanical properties ever used to determine the appropriate solid formulation?

The focus of the assessments I described in my presentation is rather on rationalising and predicting milling/compaction behaviour, including relevant possible issues and process parameters, but I believe that a formulation can also be optimised as a function of certain mechanical properties, e.g. to help flow and prevent sticking of an API that may present related problems or facilitate compaction of a very hard one.

Q3. How would you explain Rietveld refinement to a non-expert?

In most simple terms I would describe it as an iterative process whereby an initial structural model is optimised at each iteration to improve the agreement between the relevant simulated XRPD pattern vs experimental one.

Q4. I am familiar with seeing Rietveld refinement in the context of PXRD patterns. Is the technique similar for the refined ssNMR spectra that you presented?

Yes, is exactly the same thing, ssNMR is just used offline for improving the model to input into the refinement, which is ultimately still performed against XRPD data, as you maybe used to.

Aleš Medek is a physical chemist with lengthy pharmaceutical development experience. He specializes in solid state characterization of pharmaceutical solids with special interests in characterization of disorder.

He earned his PhD at UIC by developing novel multidimensional solid state NMR experiments to study materials. During his postdoctoral training at Abbott Labs Aleš devised new strategies to obtain high resolution protein structures by solution NMR. Between 2001 and 2008 he built up global SSNMR capabilities at Pfizer and developed multitude of new SSNMR pharmaceutical applications. Currently at Vertex in Boston, Aleš' interests expanded from SSNMR to all solid state characterization techniques. He is an author and coauthor of multiple scientific publications and patents.

A crystalline salt of a development stage active pharmaceutical ingredient (API) was chosen as the leading solid form, in part, to mitigate the known chemical degradation lability of the free form. To assess the risk of the salt dissociation during manufacturing or upon storage, solid state method had to be developed capable of detecting small amounts of free form in drug product in the presence of the full tablet matrix. The mechanism of the salt dissociation involved first its amorphization (to amorphous salt), followed by its dissociation in the presence of acidic tablet components into amorphous free form. As such, the solid state method had to be also able to distinguish between amorphous salt and amorphous free form. Since 19F SSNMR provided little resolution between the two amorphous forms we turned to 15N SSNMR based on nitrogen exquisite sensitivity to detect changes in a protonation state. Indeed, we found 15N SSNMR was capable of resolving both amorphous forms. API 15N isotopic enrichment was further employed to also deliver the desired method sensitivity when diluted by tablet matrix. By detecting and quantifying the salt amorphization and formation of the amorphous free form in stressed prototype tablets we were able to assess their chemical liability risks.

Q1. What is leading to the asymmetry of the F-19 crystalline FB peak on slide 14? Same question for the crystalline FB peak below?

I confirm that the Crystalline Free Form was indeed 'desolvated solvate' (i.e. prepared by the desolvation of a solvate) and as such had associated disorder. The shoulder in the 19F (and other) spectra is a manifestation of such disorder.

I graduated with a PhD from the University of Southampton looking into inorganic framework structures with a strong focus on crystallography to obtain structure-properties relationships. Since graduating I joined the leading agrochemical company, Syngenta, within the Process Studies Group based in Jealott's hill looking at polymorph discovery for new active ingredients. In this role we are looking to find new ways to crystallise and predict polymorphs and understand the influence on formulation.

Crystallisation is a common technique to isolate and purify small organic molecules in the pharmaceutical and agrochemical industries, however, there are unique challenges related to the agrochemical industry, which will be presented. Crystallisation can also be undesired and may occur throughout a product's lifecycle, for example during formulation temperature storage, spray tank mixing and droplet drying. In some instances, crystallisation can be minimised by understanding the relative stability and transitions between solid-states, especially across hydrate/anhydrate boundaries. In agrochemicals there are a range of formulation types and the desired solid-state is driven by formulation stability which can result in complex mixtures of solid forms.

Q1.We read a lot about the use of Artificial Intelligence (AI) for drug discovery. Is there a similar adaptation of AI in the Ag industry?

Yes the process of discovery is very similar between the industries with computation design at the fore front.

Q2. What is the most important physicochemical property of a solid form (e.g., solubility) in the Ag industry?

Bioavailability is the most important which is a combination of solubility, volatility and LogP.

Q3. What mechanical properties are important for granules and other solid Ag formulations?

A robust granule that isn’t susceptible to attrition to minimise dust, but that disintegrates easily on dilution in the spray tank.