Modelling and Prediction of Bacterial Attachment to Polymers

Abstract

Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, 'no touch' surgical procedures, and development of materials with inherent low pathogen attachment.The search for new materials is increasingly being carried out by high throughput methods.Efficient methods for extracting knowledge from these large data sets are essential. We used data from a large polymer microarray exposed to three clinical pathogens to derive robust and predictive machine-learning models of pathogen attachment. The models could predict pathogen attachment for the polymer library quantitatively. The models also successfully 22222222224222 predicted pathogen attachment for a second-generation library, and identified polymer surface chemistries that enhance or diminish pathogen attachment.

Validating a Predictive Structure–Property Relationship by Discovery of Novel Polymers which Reduce Bacterial Biofilm Formation

“…To date, this process has been very successful in identifying a class of monomers that surpass conventional silicone catheters for preventing catheter‐associated urinary tract infections, resulting in the granting of a CE mark for a urinary catheter device . To guide synthesis beyond the commercially available compounds computational modeling has been used to generate structure–function relationships that can predict the biological performance of virtual materials . In the case of bacterial biofilm formation a simple parameter combining the partition coefficient (log P ) and the number of rotatable bonds for hydrocarbon acrylate pendant groups pointed towards a route to a more targeted approach for materials discovery, although until now this has not been experimentally validated ( Figure a) .…”

Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches

“…[5][6][7][8][9][10][11][12] Rational materials design to accelerate materials development processes is attracting much attention, initiated to some extent by growing interest in alternative green energy storage and conversion technologies, [13][14][15][16][17][18][19][20][21] which has resulted in a strong demand for methodologies that can ascertain materials candidates with tailored properties; cheminformatics (or materials informatics) can be used as an efficient tool in this respect. [14][15][16][17][18][19][20][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] There are a number of requirements 40 that materials must satisfy to be used as solid electrolytes in all-solid-state batteries: high total Li + -ion conductivity (s Li 4 10 À4 ) with a transference number close to unity (s Li /s total E 1), a high electrochemical stability window (B6 V) with elemental Li or Li alloy, negligible electronic conductivity (s e o 10 À10 ), retention of the electrode/ electroly...…”

Adrien Albert Award: How to Mine Chemistry Space for New Drugs and Biomedical Therapies

“…[59] We used these data to construct linear and nonlinear models for cellular adhesion as a function of polymer surface chemistry for each pathogen type. [60] As Fig. 5 shows, we could make accurate quantitative predictions of the degree of bacterial adhesion for the three common pathogens, and could determine which aspects of the surface chemistry favoured low attachment.…”