Label-free real-time biomolecular interaction analysis has become a cornerstone of research and analysis in many disciplines since the release of the first SPR-based instrument almost two decades ago. As the SPR-based biosensing field has developed, there has been a strong trend towards increasing throughput by integrating higher numbers of sensing channels and parallel sample processing. This increases the complexity of biosensing systems and adds significantly to instrumentation cost. High throughput can be obtained without adding parallel sensing channels and samplers.
For example, the SensíQ Pioneer system (ICx Nomadics, Oklahoma City, Okla.) uses a highly reproducible, single-probe autosampler/injector system to generate a continuous gradient in analyte concentration as the analyte is transported through a microfluidic conduit to the sensing channels. This continuous analyte gradient injection represents a wide concentration range and replaces the conventional analyte dilution set that requires 6-10 vial positions in the sample rack. Eliminating dilution series preparation makes it feasible to kinetically screen 192 separate analyte samples in one assay run. The associated reduction in experimental complexity and assay time rivals the performance of more complex and expensive instruments with parallel sampling capabilities while providing higher data quality. Furthermore, addition of parallel sensing channels (>20) can result in reduced sensitivity, negating any benefits afforded by increased parallelism. Sensitivity is key when considering screening and kinetic characterization of low-molecular-weight chemical entities. The SensíQ Pioneer uses an SPR detector sensitive to < 1 x 10-7 refractive index units that can monitor fast binding/dissociation interactions of very small (<100 Da) or large analytes (>1 µm particle) over a very broad range of affinities (pM–mM).
Critical assessment of biosensing instruments is not easy, but there are several indicators of good performance that can be used to reliably rank any system. The issues pertinent to general data quality are discussed using a study conducted on the SensíQ Pioneer system of the interaction of immobilized carbonic anhydrase with the low-molecular-weight inhibitor carboxybenzenesulfonamide (CBS) (Figure 1). Binding interaction curves for nine concentrations of CBS, in duplicate, were analyzed. The duplicate curves superimpose perfectly, indicating excellent reproducibility. The data displays a high signal-to-noise ratio (400:1) indicating a low detector limit of detection and high-capacity surface chemistry. The stable, undulation-free baseline prior to sample injection shows good quality reference curve subtraction and low thermal drift. The plateau in the response at each concentration reflects the existence of a steady state condition for the complex but also indicates easy accessibility of analyte to immobilized enzyme. However, the single greatest indicator of data quality is the almost perfect agreement between the kinetic model fit (red curves) and the data (black curves). This interaction is known to obey the simple 1:1 interaction model so a global fit of the kinetic parameters (Ka, Kd, Rmax) should fit perfectly when no system artifacts contribute.
Mass transport limitation in biosensing flow cells inherently increases the error in fitted kinetic constants. High mass transport flux of analyte from the bulk liquid to the interaction surface requires nL scale flow cells. Two high-affinity data sets from the SensíQ Pioneer for the interaction of an immobilized receptor with a scFv (27 kDa) demonstrate the importance of nanoscale flow cells (Figure 2). Each plot shows binding response curves for six serial tripling dilutions injected in duplicate over two sensing channels with different quantities of receptor. The global 1:1 interaction model fits both data sets almost perfectly and returns very similar kinetic values. Mass transport limitation would manifest as a systemic deviation from the model, with a more severe deviation for the higher capacity surface.
Filed Under: Drug Discovery
