The pharmaceutical industry is constantly under pressure to satisfy demands of the healthcare sector and to ensure business survival in terms of return on R&D investment. The main objective is to deliver drug candidates of the highest possible quality to decrease the risk of failure in clinical phases during the development process. The authors of this review have analysed the current key strategies emerging from the different R&D approaches to achieve this objective.
A notable change that has occurred in the pharmaceutical industry over the last few decades is the move from the drug discovery process that was exclusively conducted within pharmaceutical organizations to the current open innovation model, characterised by strong collaborations among pharmaceutical industries, academia and other industries with the development of a new and sustainable funding model with public and academic participations.
The focus of current R&D business relies on a costly, risky and time-consuming strategy to ﬁnd ﬁrst-in-class medicines, based on the discovery of new targets, with the aim to treat diseases with as-yet no treatment or to deliver more efficacious drugs in the pipeline than those currently on offer. This addiction to blockbuster drugs can be partly explained by the pharmaceutical industry needs to produce sustainable revenues and returns on investment for shareholders.
However, projects based on new targets have less probability to reach the market than those based on known targets, and the later entrants – where research is based on known targets – displace the ﬁrst-in-class products in the market place, even if the ﬁrst entrant has the exclusivity free of competition during a period of time. The focus on ﬁrst-in-class products based on new targets alone is not enough to solve the current productivity gap. These days, pharmaceutical companies producing such ﬁrst-in-class drug candidates often develop a ‘follow-on’ drug based on the more validated (with the ﬁrst drug) target to reduce the risk of obtaining no drug approval. Getting the right balance between ﬁrst-in-class products based on new targets and second-in-class products, as well between target-based and phenotypic approaches, constitutes a lifeline offering the chance to resolve the drug pipeline attrition.
Failures in clinical trials have soared over the past 20 years, with attrition rates between 1990 and 2010 increasing for Phase I from 33% to 46%, for Phase II from 43% to 66% and for Phase III from 20% to 30%. The main reasons for failure (Fig. 1) at Phase II are insufﬁcient efﬁcacy (51%), safety concerns (19%) and strategic issues (29%); and reasons for Phase III failures are predominantly insufﬁcient efﬁcacy (66%) and safety concerns (21%). The failures during early stages are less costly than those at Phase III; therefore implementing strategies to identify them as early on in the process as possible is absolutely crucial.
Figure 1. Main reasons for clinical failures by Phase based on 410 drugs that entered human testing between 2000 and 2009. The main failures in Phase II and III studies are efﬁcacy issues, 54% and 2%, respectively. Safety issues represent about one-third of all the 410 drugs analysed in Phase I and Phase III studies, versus 17% of all Phase II studies.
A new model is emerging in pharmacological research called polypharmacology, which describes the activity of compounds at multiple targets. The aim of multi-targeted approach is to avoid adverse side effects (safety parameters), and to improve therapeutic efﬁcacy, prevent drug resistance or reduce therapeutic-target-related adverse side-effects (efﬁcacy parameters). Generally, multitarget drugs – in combination or not – are more efﬁcacious than single-target drugs, for instance in oncology and against viral infection. Furthermore, rather than a one-target therapy, polypharmacological modulation of a network of targets is actually required in the treatment of many multigenic diseases, as in the case of multikinase inhibitors that can block multiple targets in parallel signaling pathways and thereby prevent drug resistance caused by mutations or expression changes.
The failures due to pharmacokinetics/bioavailability issues currently account only to 1% for Phase II, reflecting the quality of the research process. The key points to consider in the drug discovery process are the right pharmacologic target and the right chemical lead. Reduction of timelines and cost (R&D) are strongly related to the high quality of science. The quality of the bioactive molecule can be evaluated using several criteria such as e-ADME proﬁle including PK/PD behaviours, metrics, drug-like concept. The improvement of the quality of the target and bioactive molecule decrease the probability of failure in clinical trials.
The authors proposed (Fig. 2) a simple overview for new hit, lead and drug optimization process using the space concept strategy and several metrics to qualify these different chemical entities, based on the Lipinski and Hopkins concept of navigation and exploration of the chemical space.
The chemical space can be subdivided into four clusters associated with several speciﬁc chemical and physicochemical properties or topological descriptors as recognition patterns such as MW, clogP, number of hydrogen acceptors (NHA) and number of hydrogen donors (NHD) to deﬁne these discrete areas. Thus, druglike chemical space (Ro5, oral route, grey cube), lead-like chemical space (Ro4, green cube), fragment chemical space (Ro3, bright blue cube) and one mauve cube dedicated to the Ro50 for transdermal drugs can be represented. Other spaces dedicated to other administration routes can also be used (mauve cube). Inside the Ro5 (clinical candidates), Ro50 (clinical candidates) and Ro4 (leads), the use of the ligand efﬁciency (LE) as a simple indices or metric to quantify the molecular quality of the different chemical entity types, for selection and optimization inside each cluster. Other indices can be also used such as LLE, BEI/SEI, SIHE or QED. As shown, the optimization navigation process between each cluster can be performed using LE, LLE or BEI/SEI. Based on these metrics, construction of more ‘druggable’ libraries (from fragments, leads or drugs) can be developed. The overlapping of the drug-likeness chemical space continuum (Ro5, grey cube) and the 3D ‘target classes’ (block-cylinder, also called target space) including individually, for instance, PPI space, kinase space, G-protein-coupled receptor (GPCR) space, etc., deﬁned an overlap volume (green rays, truncated cylinder) for which all the compounds (virtual or real) within this space are druggable; the anti-overlap area corresponds to the poorly druggable compounds. The same parameters used to deﬁne the boundaries of druggable compounds (e.g. Ro5) can be used to deﬁne a speciﬁc target space including drugs.
Figure 2. Simple overview for new hit, lead and drug optimization process using the space concept strategy (real and virtual) and metrics to qualify chemical entities.
And to conclude, the authors stress the valid point made earlier by Elebring that ‘too much process thinking in drug discovery, such as Ro5, Ro3, etc., can block enthusiasm, creativity, intuition, innovation and serendipity’. Future success in drug discovery must therefore depend on achieving the correct balance between ‘doing the right things and doing the things right’.
Blog written by Irina Chuckowree