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6 Pharmaceutical Technology ® Trends in Formulation 2022 eBook PharmTech.com Development system ic exposure dur i ng t he 'pre-IND' [i nvest i- gational new drug] phase," Maraschiello says. The formulation strategy may continually evolve during clinical development; but, in essence, a drug devel- oper must have a definitive "commercial" formula- tion at the Phase II stage, Maraschiello asserts. There are several key challenges that are signifi- cantly slowing the route to success for drug discovery programs, the first of which is the need to compen- sate for the high late-stage failure rate of candidate therapeutics, according to Tatiana Tiago, PhD, Creop- tix product manager, Malvern Panalytical. "Several remedial strategies have emerged to drive earlier failure of unfavorable molecules and expand- ing and improving the starting pool of candidates in order to survive fierce attrition," Tiago states. High-throughput screening (HTS) is a prominent example of how the industr y is expanding and im- proving the star ting pool of candidates, Tiago ex- plains; but, while HTS has been a staple of the sector for many years and continues to evolve, its full poten- tial is often limited by several hurdles. Tiago explains that a lack of robust and sensitive assays is one hur- dle, which is made worse by the fact that many de- vices sacrifice sensitivity as throughput is increased. Secondly, HTS can still be long and time-consuming, often requiring several weeks to progress from a hit to a lead. Tiago also explains that timelines are fur- ther drawn out by complex data analysis. "Even if all these challenges can be overcome, re- searchers are of ten working with poor compound library diversity," Tiago says. "In exploring HTS ap- proaches, researchers must be careful they are not just expanding their candidate pool with poor-qual- ity, non-viable candidates—all at considerable time and resource costs." In one aspect of drug discovery, there is the growth of f ragment-based dr ug discover y (FBDD), which identifies small, low molecular weight molecules or "fragments" that bind weakly to biologically relevant targets, before leads are then "grown" into drug-like compounds, explains Tiago. "FBDD offers attractive benefits, from requiring smaller starting pools of compounds and unlocking better avenues for lead optimization to being able to address previously intractable targets, such as membrane proteins," Tiago says. "However, FBDD can have significant hurdles of its own. High tem- poral resolution is needed for detection, and—with ligand-to-analyte molecular weight ratios exceeding 1000:1—changes are undetectable to all but the most sensitive methods." The most common detection technologies, such as surface plasmon resonance and biolayer interferom- etry, often struggle with these sensitivity and reso- lution challenges, Tiago asserts. Meanwhile, another challenge has resulted from the move from broad por tfolios spanning diverse disease areas to por t- folios that are more focused on promising growth areas—for example, immuno-oncology. "T h is mea ns orga n izat ions now need a deeper k nowledge of t a rget d i sea se pat hway s a nd d i s- ease-appropriate assays. This dovetails with an in- dustr y-wide drought of appropriately skilled staff that can operate in niche domains using highly spe- cialized tools and techniques. Without that compe- tence and expertise, the problem of cumbersome data analysis will persist," Tiago predicts. Advancing from discovery to clinical In Tiago's view, one of the most important advance- ments for drug discovery bioanalytics is grating-cou- pled interferometry (GCI), which is a relatively new biophysical characterization method for label-free molecular interaction analysis. This method builds on waveguide interferometry, "measuring refractive index changes in an evanescent field resulting from ligand-analyte interactions to determine binding af- finity and kinetic rates," she says. Tiago argues that label-based approaches to screen- ing, such as t he enz y me-lin ked im munosorbent assay, can also suffer from a high signal-to-noise ratio, which harms the sensitivity of such approaches. Label-free technologies, such as GCI, have been in- creasingly replacing and complementing label-based affinity screens however, offering better sensitivity as well as being capable of reaching increasingly high levels of throughput, Tiago says. "Label-free technologies eliminate the risk of label interactions and detects binding events from across the entire sensor surface leading to a greater num- ber of binding events. This, combined with the better signal-to-noise ratio provided by GCI, allows greater sensitivity to detect interactions precisely and con- sistently. In this way, GCI is also addressing some of the challenges mentioned above," Tiago states. Moreover, G CI i s a l so en abl i ng t he abi l it y of drug candidates to treat disease targets that were prev iou sly con sidered u nd r ug gable, or u nt reat- able, such as membrane protein targets, Tiago adds. "By using a type of microf luidics that doesn't clog, GCI lets researchers more easily work with unpuri- fied samples, such as those containing membrane proteins," she says. Meanwhile, Maraschiello points out that an im- proved abi l it y to col lec t a nd i ntegrate data t hat support in-silico modeling for prediction and selec- tion, which has been bolstered by artificial intelli- gence, is a key technological improvement. However, the secret to increasing the success of the late discov- ery stages (i.e., the nomination of a pre-clinical drug candidate [PDC] with the right ef ficacy and right