Today, we are experiencing a diversified drug delivery landscape. Many drug molecules are produced in biotechnological production processes, are unstable, or exhibit poor aqueous solubility. The need for innovative formulation approaches has continuously grown. Smarter drug delivery systems require advanced analytical technologies as well. Our team combines data science and drug delivery research to make accurate predictions of how new formulations will perform in patients. Computer models and simulations help us to better understand the behavior of dosage forms and improve their quality and safety.
“Who controls the past controls the future: who controls the present controls the past.”
– George Orwell –
First outlined by Joseph M. Juran, quality-by-design (QbD) suggests the continuous monitoring and improvement of drug manufacturing. Biopredictice tools play a key role in the development of medicines by creating a feedback loop in the development pipeline. TheWackerLab specializes in this key area of formulation development
NanoDelivery uses structures in the nanoscale to deliver drugs to target sites in the human body. These tiny particles are difficult to design and, more often, a better understanding of the attributes that matter for the in vivo performances is required. Recent examples include the COVID vaccines, long-acting injectables, and liposomes. To synthesize these novel therapeutics we are using scalable and GMP-compliant microfluidic technologies.
The performance of drug products depends on a wide variety of parameters such as the drug release or the physical stability of the delivery system. Our team develops novel biopredictive performance assays. The dispersion releaser (DR) technology was one of our first inventions and has been commercialized. At present, we are working with leading manufacturers of dissolution instruments to progress in this important area.
The integration of computational methods and data science into the pharmaceutical sciences supports our understanding of drug delivery. Here, we custom-design algorithms based on the type and density of data associated with drug delivery systems. Clinical trials with nanomedicines, for instance, often involve a very limited number of patients. In such cases, a smart model design instead of data mining strategies can be applied. Our key aim is to gain deeper insight into the mechanisms that lead to successful drug delivery.
Building bridges between existing data and our drug delivery pipeline, we identify relevant physiological processes that affect in vivo absorption and establish validated in vitro-in vivo correlations (IVIVCs). These IVIVCs provide the ultimate proof of the predictive power of our in vitro assays and can be used to estimate the behavior of new formulations in a virtual patient population.