The Pharma Lab Show

Today, we take a different approach on the Pharma Lab show. There are a lot of challenges when it comes to making mixture standards for solid state analytical methods. Simon conducts a live episode where he discusses the different approaches and how to evaluate them.
Join us as we discuss:
Requirements for the ideal solid state mixture standards
Laboratory mixed standards are usually the largest source of error in a method
How to evaluate sources of error from measurement apparatus and sample
Standardless methods and component analysis.
Follow along with the video here: https://pharma.rigaku.com/insights/pharma-lab-show-mixtures-and-standards
For more insights into the pharmaceutical industry, subscribe to the show on Apple Podcasts, Spotify, or wherever podcasts are found.
Listening on a desktop & can’t see the links? Just search for The Pharma Lab Show in your favorite podcast player.

How far is the Pharmaceutical 3D Printing Revolution? In this episode, Simon speaks with Professor Mo Maniruzzaman, Assistant Professor in Pharmaceutics and Drug Delivery Assistant Professor in Pharmaceutics and Drug Delivery, The University of Texas at Austin.
They speak about:
How he became passionate about 3D printing
How far are we from personalized medicine through 3D printing?
How will Personalized “Pill Printing” evolve as the industry grows

Crystal structure prediction (CSP) has been a vital part of the mission of XtalPi, an artificial intelligence and quantum physics based pharmaceutical technology company aiming to improve the speed, scale, novelty, and success rate of drug discovery and development.
Chandler Greenwell, an applications scientist at XtaPi, shares how CSP is revolutionizing the field.
We discuss:
How polymorphism relates to CSP
Insights that CSP can provide for de-risking the polymorph landscape
Future applications for CSP
Resources mentioned during the podcast:
Facts and fictions about polymorphism
Why don’t we find more polymorphs?
Report on the sixth blind test of organic crystal structure prediction methods
Harnessing Cloud Architecture for Crystal Structure Prediction Calculations
Prediction of the Relative Free Energies of Drug Polymorphs above Zero Kelvin
Selecting a stable solid form of remdesivir using microcrystal electron diffraction and crystal structure prediction
Virtual Coformer Screening by Crystal Structure Predictions: Crucial Role of Crystallinity in Pharmaceutical Cocrystallization
Guiding Lead Optimization for Solubility Improvement with Physics-Based Modeling
Email Chandler: [email protected]
For more insights into the pharmaceutical industry, subscribe to the show on Apple Podcasts, Spotify, or wherever podcasts are found.
Listening on a desktop & can’t see the links? Just search for The Pharma Lab Show in your favorite podcast player.

Spray drying has become one of the go-to techniques for the manufacture of amorphous dispersions and inhalable formulations due to the ability to control solvent content, morphology, size, and density of the resulting particulates in the dried powder.
X-ray computed tomography is a new technique for this spray dried dispersion application.
Peter Oberta, Managing Director at Rigaku, co-authored the study on this new approach and he joins the show to explain everything you need to know.
We discuss:
- The advantages of using this technique
- How to control particle size, morphology, and density using x-ray CT
- How much sample preparation is necessary for x-ray CT
- The qualitative results of the study
- The resolution that can be achieved with x-ray CT
- The data treatment required to complete the analysis
Mentioned during the podcast:
- Characterization of the Morphological Nature of Hollow Spray Dried Dispersion Particles Using X-ray Submicron-Computed Tomography
- X-Ray CT Imaging
- DigiM
For more insights into the pharmaceutical industry, subscribe to the show on Apple Podcasts, Spotify, or wherever podcasts are found.
Listening on a desktop & can’t see the links? Just search for The Pharma Lab Show in your favorite podcast player.

For successful rational indexing, you can’t just make a guess and refine. Each unit cell is defined by six parameters, meaning there could be billions of potential solutions. Your probability of being correct is abysmal.
Your best approach is to use advanced indexing tools. Machine learning can transform rational indexing from an intellectual challenge into the click of a button.
In this episode, Simon Bates , VP of Science and Technology at Rigaku, speaks with us about the problem that rational indexing solves, its uses in pharma since the 1940s, and near-future breakthroughs that will make it more efficient.
We discuss:
- Using diffraction data to identify crystal unit cells
- How rational indexing reduces the search space
- Why pharma should focus on identifying crystal unit cells
- The evolution of advanced indexing tools
For more insights into the pharmaceutical industry, subscribe to the show on Apple Podcasts, Spotify, or w herever podcasts are found.
Listening on a desktop & can’t see the links? Just search for The Pharma Lab Show in your favorite podcast player.