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Speeding Up Early Stage Biotherapeutic Discovery with Next Generation Differential Scanning Fluorime
日期：2024-04-27 11:02
浏览次数：76
摘要：Limited amounts of sample often hinder the analyses needed for the selection and optimization of the candidates in the early stages of biotherapeutic discovery. Examples of analyses are activity (often binding the target and causing a change in the response to normal function) and stability (potential to survive both the manufacturing process and longer-term storage) The SUPR-DSFfrom Protein Stable eliminates these challenges and combines low sample usage and highly informative data on the stab
Limited amounts of sample often hinder the analyses needed for the selection and optimization
of the candidates in the early stages of biotherapeutic discovery. Examples of analyses are activity
(often binding the target and causing a change in the response to normal function) and stability
(potential to survive both the manufacturing process and longer-term storage).
Existing technologies for measuring stability of biologics have all proved useful in identifying
candidates that have higher stability than others under certain parameters but have been
compromised in some way. There are technologies which give excellent results but may be slow
and unsuitable for larger numbers of samples. Others can work on higher numbers of samples,
but perhaps lose some data integrity or use too much sample to be a viable option. The SUPR-DSF
from Protein Stable eliminates these challenges and combines low sample usage and highly
informative data on the stabilities of thousands of samples. The use of the industry-standard
format of 384-well microplates eliminates the expensive and risky steps of manual handling by
combining sample preparation and analysis in the same plate. Fitting seamlessly into existing
processes, stabilities of thousands of samples can be measured and compared in a matter of hours,
not days and weeks.
To illustrate the use of the SUPR-DSF for variant comparison and selection in early-stage discovery,
we have used a model protein system to compare 16 analogues[1] and identify the most stable
of the candidates in the early stages of biotherapeutic discovery. Examples of analyses are activity
(often binding the target and causing a change in the response to normal function) and stability
(potential to survive both the manufacturing process and longer-term storage).
Existing technologies for measuring stability of biologics have all proved useful in identifying
candidates that have higher stability than others under certain parameters but have been
compromised in some way. There are technologies which give excellent results but may be slow
and unsuitable for larger numbers of samples. Others can work on higher numbers of samples,
but perhaps lose some data integrity or use too much sample to be a viable option. The SUPR-DSF
from Protein Stable eliminates these challenges and combines low sample usage and highly
informative data on the stabilities of thousands of samples. The use of the industry-standard
format of 384-well microplates eliminates the expensive and risky steps of manual handling by
combining sample preparation and analysis in the same plate. Fitting seamlessly into existing
processes, stabilities of thousands of samples can be measured and compared in a matter of hours,
not days and weeks.
To illustrate the use of the SUPR-DSF for variant comparison and selection in early-stage discovery,
we have used a model protein system to compare 16 analogues[1] and identify the most stable
ones for further processing.
Results
The SUPR-DSF produced high quality melting curves for all 16 samples, which included a
Wild Type (WT) protein and 15 single-point mutation variants. The calculated melting
temperatures agreed to within 0.2°C for the triplicate data. The fraction unfolded curves for all 15
mutants (Orange) compared to WT (Blue) are in Figure 1. Three of the mutants (9, 13, and 14)
decreased the stability of the WT protein, which is easily seen by the shift to the left in the melting
curve. The remaining 12 mutants all increased the stability of WT protein with Mutants 1, 8, and 12
showing the largest increase in stability of WT protein with Mutants 1, 8, and 12
showing the largest increase in stability
