Biology subjects

von Recum, H.

Publications and source records attributed to von Recum, H..

3 recordsLinked to original sources

bioRxiv2020

Leveraging Affinity Interactions to Prolong Drug Delivery of Protein Therapeutics

While peptide and protein therapeutics have made tremendous advances in clinical treatments over the past few decades, they have been largely hindered by their ability to be effectively delivered to patients. While bolus parenteral injections have become standard clinical practice, they are insufficient to treat diseases that require sustained, local release of therapeutics. Cyclodextrin-based polymers (pCD) have been utilized as a platform to extend the local delivery of small-molecule hydrophobic drugs by leveraging hydrophobic-driven thermodynamic interactions between pCD and payload to extend its release, which has seen success both in vitro and in vivo. Herein, we proposed the novel synthesis of protein-polymer conjugates that are capped with a "high affinity" adamantane. Using bovine serum albumin as a model protein, and anti-interleukin 10 monoclonal antibodies as a functional example, we outline the synthesis of novel protein-polymer conjugates that, when coupled with cyclodextrin delivery platforms, can maintain a sustained release of up to 65 days without largely sacrificing protein structure/function which has significant clinical applications in local antibody-based treatments for immune diseases, cancers, and diabetes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/410621v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@25ad06org.highwire.dtl.DTLVardef@1827b8eorg.highwire.dtl.DTLVardef@17dd798org.highwire.dtl.DTLVardef@d54d55_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Predicting Drug Interactions to Unassociated Biomedical Implants Using Machine Learning Techniques and Model Polymers

Affinity based drug delivery mechanisms increase efficacy and minimalize off target effects when compared to non-specific methods due to the localization of drugs within target areas. While this is beneficial for targeted delivery, introduction of foreign polymeric medical devices into the body provide a potential area of localization due to high affinity between administered drugs and polymers. Previous attempts at creating models to predict affinity between small molecule drugs and polymers require a specific model be trained for each individual polymer failing to incorporate input features of both the polymer (host) and small molecule drug (guest). Within, we propose a universal model built using a neural network and quantitative structure activity relationships to predict the binding energy between guest and host molecules using input features. The trained model returned a correlation value, R2, of 0.9806 and 0.9958 between predicted and experimental binding affinity for the training and validation sets, respectively. This correlates to a mean absolute error of 0.951 kJ/mol and 0.771 kJ/mol for the training and validation sets, respectively. While limited to the current polymers used to train the model, the dataset can be expanded, and models retrained for further applications.

bioengineering↗

bioRxiv2019

Using QSAR for predictions in drug delivery

Drug delivery research is an inherently empirical process, however high-throughput approaches could take advantage of understanding drug/material interactions such as from electrostatic, hydrophobic, or other non-covalent interactions between therapeutic molecules and a drug delivery polymer. Cyclodextrin polymers have been investigated for drug delivery specifically due to their capacity to exploit this affinity interaction to change the rate of drug release. Testing drug candidates; however, for affinity is time-consuming, making computational predictions more effective. One option, molecular \"docking\" programs, provide predictions of affinity, but lack reliability, as their accuracy with cyclodextrin remains unverified experimentally. Alternatively, quantitative structure-activity relationship models (QSARs), which analyze statistical relationships between molecular properties, appear more promising. Previously constructed QSARs for cyclodextrin are not publicly available, necessitating an openly accessible model. Around 600 experimental affinities between cyclodextrin and guest molecules were cleaned and imported from published research. The software PaDEL-Descriptor calculated over 1000 chemical descriptors for each molecule, which were then analyzed in R to create several QSARs with different statistical methods. These QSARs proved highly time efficient, calculating in minutes what docking programs would take hours to accomplish. Additionally, on test sets, QSARs reached R2 values of around 0.7-0.8. The speed, accuracy, and accessibility of these QSARs improve evaluation of individual drugs and facilitate screening of large datasets for potential candidates in cyclodextrin affinity-based delivery systems. An app was built to rapidly access model predictions for end users using the \"shiny\" library in R. To demonstrate the usability for drug release planning, the QSAR predictions were coupled with a mechanistic model of diffusion within the app. Integrating new modules should provide an accessible approach to use other cheminformatic tools in the field of drug delivery.

bioengineering↗