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An early drug candidate screening strategy should incorporate clear targets to lessen late-stage failure.
Time-to-market remains a significant factor in drug development today, as delays in timelines can often have costly and damaging effects, not only to the drug developer but to the patient. Challenges in the drug development cycle arise early in the drug candidate screening process, but how are industry players using technology to enhance workflows and shorten time-to-market?
The biggest challenges in early-stage drug candidate screening are always the same when referring to small molecules—that is, new chemical entities (NCEs), says Ciriaco Maraschiello, PhD, executive vice-president and global head of drug development at Evotec, a Germany-based life science company. These challenges include safety, bioavailability (in the case of oral dosages), and the translation of both efficacy and safety from pre-clinic to clinic.
Regarding large molecules, in particular monoclonal antibodies (mAbs), translation is still an issue, Maraschiello notes. He further explains that the formulation aspects of large-molecule drug candidates are even more important than with NCEs because of the “commercial decision.” The formulation must be developed in parallel to the lead optimization efforts in the discovery phase for large molecules, with the selected formulation being already established as the definitive formulation at the early stages of development, Maraschiello adds.
“The formulation is indeed an inherent part of the efficacy of a mAb. In the case of [NCEs], it is important to develop formulations that ensure the highest systemic exposure during the ‘pre-IND’ [investigational new drug] phase,” Maraschiello says. The formulation strategy may continually evolve during clinical development; but, in essence, a drug developer must have a definitive “commercial” formulation at the Phase II stage, Maraschiello asserts.
There are several key challenges that are significantly slowing the route to success for drug discovery programs, the first of which is the need to compensate for the high late-stage failure rate of candidate therapeutics, according to Tatiana Tiago, PhD, Creoptix product manager, Malvern Panalytical.
“Several remedial strategies have emerged to drive earlier failure of unfavorable molecules and expanding 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 industry is expanding and improving the starting pool of candidates, Tiago explains; but, while HTS has been a staple of the sector for many years and continues to evolve, its full potential is often limited by several hurdles. Tiago explains that a lack of robust and sensitive assays is one hurdle, which is made worse by the fact that many devices 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 further drawn out by complex data analysis.
“Even if all these challenges can be overcome, researchers are often working with poor compound library diversity,” Tiago says. “In exploring HTS approaches, researchers must be careful they are not just expanding their candidate pool with poor-quality, non-viable candidates—all at considerable time and resource costs.”
In one aspect of drug discovery, there is the growth of fragment-based drug discovery (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 temporal 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 interferometry, often struggle with these sensitivity and resolution challenges, Tiago asserts. Meanwhile, another challenge has resulted from the move from broad portfolios spanning diverse disease areas to portfolios that are more focused on promising growth areas—for example, immuno-oncology.
“This means organizations now need a deeper knowledge of target disease pathways and disease-appropriate assays. This dovetails with an industry-wide drought of appropriately skilled staff that can operate in niche domains using highly specialized tools and techniques. Without that competence and expertise, the problem of cumbersome data analysis will persist,” Tiago predicts.
In Tiago’s view, one of the most important advancements for drug discovery bioanalytics is grating-coupled 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 affinity and kinetic rates,” she says.
Tiago argues that label-based approaches to screening, such as the enzyme-linked immunosorbent 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 increasingly 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 number of binding events. This, combined with the better signal-to-noise ratio provided by GCI, allows greater sensitivity to detect interactions precisely and consistently. In this way, GCI is also addressing some of the challenges mentioned above,” Tiago states.
Moreover, GCI is also enabling the ability of drug candidates to treat disease targets that were previously considered undruggable, or untreatable, such as membrane protein targets, Tiago adds. “By using a type of microfluidics that doesn’t clog, GCI lets researchers more easily work with unpurified samples, such as those containing membrane proteins,” she says.
Meanwhile, Maraschiello points out that an improved ability to collect and integrate data that support in-silico modeling for prediction and selection, which has been bolstered by artificial intelligence, is a key technological improvement. However, the secret to increasing the success of the late discovery stages (i.e., the nomination of a pre-clinical drug candidate [PDC] with the right efficacy and right safety) involves the combination of an appropriate mix of studies pulled together by drug discoverers and drug developers.
“The appropriate screening cascade will create the opportunity to reduce the attrition of the drug candidate during the pre-clinical phase. The same concept applies when the PDC moves into the clinical development phase,” Maraschiello says.
In the clinical development phase, the translational approaches supported by data management and statistics are the key to correctly designing clinical trials with back-and-forth loops between the pre-clinical and clinical phases, Maraschiello adds.
To optimize early drug candidate screening results, goals must be firmly established. For instance,
it must be clear from the start which type of patient population is the target. “From the start” means that, at the very early stages of drug discovery, a clear target product profile and a defined regulatory strategy plan must be in place when an R&D process is started, according to Maraschiello.
From the starting point, a developability assessment must be established, which occurs at the lead optimization stages, where a compound or a series of lead compounds are thoroughly tested for their potential to become a therapeutic product. In the case of advanced therapy medicinal products, market access considerations are even more important and must happen early in the R&D process, emphasizes Maraschiello.
“Consequently, what is important is not the quantity but the quality of the molecules because of an intelligent screening based on the premises mentioned above,” says Maraschiello.
The ideal early screening strategy encompasses two key goals: high throughput and high-quality hits, according to Tiago. Such an approach speeds the route to market in two main ways: by having a larger pool of candidates that can weather later waves of attrition without being fully depleted, and by minimizing the time and resources spent progressing sub-optimal candidates.
The insight needed to drive hit quality requires sensitivity and performance that isn’t available with many current instruments and methods, Tiago points out.
“Extracting such insight at scale generates unfathomable volumes of data that can paralyze analysts,” Tiago states.
GCI helps by providing binding kinetics information in a single method at the initial stages of screening, allowing researchers earlier—and deeper—insight into how the candidate might behave in a biological system. In the end, this process leads to better decision-making and eliminates the need for successive and distinct affinity and assay screens, which can have contradictory results and can be extremely time intensive.
“GCI data analysis is being streamlined with automated platforms to speed analysis while increasing accuracy and reproducibility. These automated tools are tackling the speed-to-market challenge by reducing the impact of skilled labor shortages and mitigating the risk of later-stage issues owing to human error or unreliable analysis results,” Tiago explains.
Successful early candidate screening means the successful nomination of a high-quality pre-clinical drug candidate, emphasizes Maraschiello.
“Quality here is defined as an enhanced probability of success during the pre-IND phase,” Maraschiello says.
Maraschiello explains that when an appropriate screening cascade is conjugated with a late-optimization-phase design to de-risk the selected compounds before they enter the regulatory pre-clinical development phase, the process either ensures the selection of an IND-ready candidate or can lead to the early termination of a selected drug candidate where the impact of cost and time is made clear at the outset. Another important factor in a successful drug candidate screening process is the ability to integrate “development thinking” into the discovery phases and to develop translational approaches aimed at optimizing the clinical trial designs, says Maraschiello. This approach ultimately increases the odds of a successful Phase III clinical trial, Maraschiello points out.
Ultimately, silo thinking and sequential decisional processes within the R&D value chain should be abandoned, Maraschiello states.
“With the technologies at hand and with the multi-disciplinary character of the R&D process, it is increasingly difficult to keep working in data silos either in discovery or in development and between both phases. The R&D process is not a sequential process anymore; it is a virtuous circle going back and forth through the entire value chain to increase the productivity of the R&D process,” Maraschiello says.
Where the drug discovery and candidate screening process was difficult ten years ago, nowadays, the industry has the increased aptitude to manage pre-clinical and clinical data, which creates a powerful translational and predictive process with the sole purpose of reaching the patient faster and better, Maraschiello adds.
Meanwhile, Tiago notes that, aside from quality and throughput challenges, the one thing hurting and frustrating drug developers more than anything else is the late-stage failure of a candidate that has exquisite target activity but which fails due to other attributes, such poor manufacturability or stability.
“While the activity of your candidate at the target is a critical determinant of its therapeutic and market potential, it isn’t the only one. There are many other attributes that need deep and comprehensive consideration—particle size, shape, structure, surface properties, and thermal stability, for example,” Tiago states. These characteristics can impact behavior as diverse as reaction and dissolution, flowability, compressibility, and efficacy, and because of their impact, these characteristics need consideration as early as possible in development.
“In the case of protein-based therapeutics, stability considerations are particularly important. For example, if a protein is structurally unstable, it can undergo conformational changes that reduce binding and therapeutic efficacy. Similarly, colloidal instability within the final formulation of a protein-based therapy can lead to protein aggregate formation, which could impact delivery or even be immunogenic,” Tiago explains.
With such a complex range of attributes to navigate, developers must, therefore, use complementary and orthogonal analytical technologies to provide a full understanding of their candidates. A helpful suite of tools could include dynamic light scattering for colloidal stability evaluation, differential scanning calorimetry for structural stability measurements, and a host of other complimentary techniques, according to Tiago. Planning an orthogonal strategy is another challenge in and of itself, however. Considerations for an orthogonal strategy include:
Such considerations require a level of knowledge and expertise that spans multiple domains and is, as noted previously, in significantly short supply, Tiago emphasizes. Contract research organizations (CROs), for instance, have been stepping up to support bio/pharmaceutical organizations to navigate these key questions.
“Essentially, [CROs] are filling the industry void of specialized expertise and concentrating it in a manner that can best support drug developers. Analytical instrument providers are also helping here, providing timely and extensive advice on optimal study design and being open about the need for orthogonality in early screening. This combination, ultimately, creates a more collaborative environment—one that is carving a smoother, more strategic path to drug discovery success,” Tiago concludes.