Biologics researchers are deeply concerned with formulation. Formulation typically refers to the final development of a deliverable, biologically derived molecule (such as a protein, peptide, nucleic acid, or small molecule) as it goes to market. However, the reality for biologics researchers is that formulation is a multi-step process that considers the entire development of a biologic candidate, from early discovery to delivery to a patient.
When considering some of the earliest parts of the biologic development cycle, two parameters that many researchers rely on are particle size and dispersity in solution. This data is obtained from dynamic light scattering, or DLS experiments. If you’re struggling to optimize a candidate, it’s necessary to make some formulation changes to improve its odds of making it successfully to market. Let’s take a look at some of the typical changes a protein biologic can undergo, and how these affect the DLS read-out.
Making mutations to a protein’s primary structure is the bread and butter for many protein engineers. Mutations are made by swapping out one amino acid for another — this switch can be rational, such as when trying to enhance antigen binding by increasing ionic bonds; or random, to try to determine what changes can be made to a structural region to enhance its stability.
When it comes to DLS data, compare the mutated candidate to its parent. Look at how the dispersity (PDI) changes. A reduced PDI due to mutations indicates more single, well-folded species of proteins, and is generally a desired outcome for long-term success of a candidate.
Early discovery and development work is often done with very small sample amounts, due to cost-effective limitations in the lab. However, it’s important to note that the concentration of a biologic candidate can have a huge impact on its ultimate efficacy. Many drugs are stored and delivered to patients at higher concentrations.
DLS can help you predict how your biologic will behave at different concentrations with its ability to measure self-interaction, or kD. This information is derived from plotting the diffusion coefficient vs. protein concentration. Most developers look for a negative slope, which is a good indication that a protein will not aggregate at higher concentrations.
With the advent of personalized medicine, there is a huge sub-market of biologics interested in using biologically-derived molecules like monoclonal antibodies (mAbs) to deliver small molecules, peptides, or gene therapy nucleic acids only to specific areas in the body. In this case, the mAbs target a receptor or protein of interest in a diseased or damaged cell, and they bring the medicine with them. This requires conjugation of the medicine to the mAb. The conjugation process can be tough on a mAb, and it is often crucial to ensure no unconjugated material remains.
With DLS, you can ensure that the mAb survived the conjugation process by examining the PDI and determining whether there are large aggregates formed as a result of the harsh chemical treatments. You can also use the rH as an indication that conjugation was successful, as there is very likely to be a difference in size between the conjugated and unconjugated mAb. Finally, by examining the distribution of sizes, you can ensure that your clean-up process has removed all free, unconjugated particles.
In biologics, it is not unusual to start the development process off in a “typical” buffer for your group, usually one that is considered broadly similar to the body’s environment, such as PBS pH 7.4. However, there can be great flexibility in what buffers are acceptable once the candidate is ready for the clinic. This gives researchers lots of leeway to optimize the environment around their candidate, especially if they have less flexibility in what structural changes can be made to the biologic. Altering pH, changing the salt concentration, or adding small amounts of stabilizing ions or molecules can all have a big influence on a candidate’s behavior.
If your DLS data shows high polydispersity (whether from multiple species or large aggregates), making changes to the buffer is a great way to stabilize your candidate.
In some cases, formulations might require carrier proteins or multi-protein mixtures to increase circulation in the body or the efficacy of treatment in a patient. DLS measures all particle types in solution. By comparing kD measurement of the individual components vs their mixture, it is possible to discern whether combining two different proteins increases the chances of aggregation. Additionally, while the PDI will increase from the presence of multiple species, the distribution of sizes can help indicate whether there is aggregation induced from mixing the candidates.
Likewise, it is possible to do some basic experiments with DLS to determine if a direct interaction is taking place between the proteins, or if they do not associate in solution.
Overall, DLS measurements are a great first-pass for understanding the stability of your biologics candidates. Information from the PDI, changes in the rH, and self-association measurements are all handy tools in the formulation scientist’s toolbox for monitoring how environmental and structural changes affect the behavior of a candidate. This information can greatly increase the chances of a successful candidate earlier in the development process, improving outcomes for patients and your projects alike.
For more information on how Dynamic Light Scattering can help your biologics research, read this eBook: The biologics researcher’s guide to DLS.