52 BIOTECHNOLOGY


IMPROVED PREDICTIONS of drug efficacy


Samuel Altun, Patrik Forssén & Ian A Nicholls report on cell-based heterogeneous interaction analysis


T


he continuous development of new experimental technologies and growth in computational power


together enable new possibilities in drug discovery and development. Experimental assays can now be designed to better mimic in vivo conditions while still maintaining high experimental resolution. Consequently, more information can be obtained both from the actual experiments and from the analysis of the experimental data. In this paper we discuss the potential


for improved prediction of drug efficacy based on a combination of label-free in flow cell-based assays and heterogeneous computer interaction analysis. To elucidate the potential of this approach, we will compare the interaction profile of a typical homogenous biochemical one-to-one interaction with that of a more complex interaction observed in a cell-based experiment. For this, we have chosen to study Parathyroid Hormone (PTH) interaction with immobilised Parathyroid Hormone Receptor (PTH1R) and Trastuzumab’s interaction with HER2- expressing SKBR3 cells. For experiments, the label-free Attana Cell 200 Quartz Crystal Microbalance (QCM) biosensor (Fig. 1) was used, which can monitor the interaction of an analyte with cells or molecules immobilised on the biosensor surface as described in several publications1,2,3,4


. Tese results were then


analysed with a newly developed computer software5,6


based on Adaptive Interaction


Distribution Algorithm (AIDA). In the use of the AIDA strategy


presented here, the interaction dissociation rate is first calculated to reveal if there are any heterogeneous interactions. Tis is calculated because the dissociation rate is independent of the analyte concentration and hence has one fewer degrees of freedom and thereby is most suitable to start the analysis with. Tereafter, the AIDA is used to obtain the number of interactions for each analyte concentration.


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Tis information is then used to estimate the interaction rate constants by fitting to the measured sensorgrams one at a time. Finally, all estimated rate constants are plotted and clustered, where each cluster represents a mode of complex formation. In the first experiment, the


representative homogeneous PTH-PTH1R system was used. PTH1R is immobilised on the sensor chip and PTH is flowed over at increasing concentrations. In Fig .2A, the traditional biosensor sensorgrams are displayed showing the association and dissociation phase for the different concentrations. In Fig. 2B, the logarithm of the dissociation signal is plotted against the dissociation time of PTH from the surface, giving as expected an almost linear graph. In Fig. 2C, the logarithm of the


association rate for all the concentration against the logarithm of the dissociation rate for the different concentrations is plotted. In this case, all concentrations superimpose in one position in the graph, indicating a homogenous interaction. In the second experiment, the interaction of Trastuzumab with HER2 expressing SKBR3 cells was analysed. Fig. 3A depicts the sensorgrams for the different concentrations. In Fig. 3B, analysis of the dissociation rate clearly depicts a deviation from linearity, thus


Fig.1. The Attana Cell 200 Quartz Crystal Microbalance


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