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Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

The results of a new method developed to identify well defined structural transformations that are key to improve a certain ADME profile are presented in this work. In particular Naïve Bayesian statistics and SciTegic FCFP_6 molecular fingerprints have been used to extract, from a dataset of 1,169 compounds with known in vitro UGT glucuronidation clearance, those changes in chemical structure that lead to a significant increase in this property. The effectiveness in achieving that goal of the thus found 55,987 transformations has been quantified and compared to classical medicinal chemistry transformations. The conclusion is that on average the new transformations found via in silico methods induce increases of UGT clearance by twofold, whilst the classical transformations are on average unable to alter that endpoint significantly in any direction. When both types of transformations are combined via substructural searches (SSS) the average twofold increase in glucuronidation is maintained. The implications of these findings for the drug design process are also discussed, in particular when compared to other methods previously described in the literature to address the question 'Which compound do I make next?'

The human PEPT1 protein is an influx transporter known to influence the ADME profile of several bioactive compounds. A good example is acyclovir, which possesses high safety and selectivity but has a low bioavailability. The l-valyl ester prodrug of this compound is transported by PEPT1, resulting in a three- to four-fold increase in plasma levels. Therefore, knowledge of the Structure–Activity Relationships (SAR) of PEPT1 ligands can enable us to favourably manipulate the ADME properties of promising leads that suffer from low bioavailability. We applied three classification methods (naive Bayesian classification, recursive partitioning and linear discriminant analysis) together with the descriptors best suited for each method to a large PEPT1 inhibitor/non-inhibitor dataset. This dataset was assembled from literature and was divided into three groups: the ‘inhibitor’ group (Ki≤1 mM, 113 compounds), the ‘unknown’ group (Ki>1 mM and

Reviews of the productivity of the pharmaceutical industry have concluded that the current business model is unsustainable. Various remedies for this have been proposed, however, arguably these do not directly address the fundamental issue; namely, that it is the knowledge required to enable good decisions in the process of delivering a drug that is largely absent; in turn, this leads to a disconnect between our intuition of what the right drug target is and the reality of pharmacological intervention in a system such as a human disease state. As this system is highly complex, modelling will be required to elucidate emergent properties together with the data necessary to construct such models. Currently, however, both the models and data available are limited. The ultimate solution to the problem of pharmaceutical productivity may be the virtual human, however, it is likely to be many years, if at all, before this goal is realised. The current challenge is, therefore, whether systems modelling can contribute to improving productivity in the pharmaceutical industry in the interim and help to guide the optimal route to the virtual human. In this context, this chapter discusses the emergence of systems pharmacology in drug discovery from the interface of pharmacokinetic–pharmacodynamic modelling and systems biology. Examples of applications to the identification of optimal drug targets in given pathways, selecting drug modalities and defining biomarkers are discussed, together with future directions.

The pharmaceutical industry is in the process of re-inventing its pipeline in an attempt to overcome its increasing phase II and III attrition rates. Here, we describe how systems pharmacology can be used as a risk assessment tool to alleviate this problem before bringing in larger investments. We propose that this translational research tool could provide a valuable, complementary addition to other emerging innovative approaches for target identification and validation in discovery and, ultimately, for aiding clinical trial optimization.

Data mining by pairwise comparison of over 150,000 human liver microsome (HLM) intrinsic clearance values stored within the internal Pfizer database has been performed by an automated tool. Systematic probability tables of specific structural changes on the intrinsic clearance of phenyl derivatives have been generated. From these data two new parameters, the Pfizer Metabolism Index (PMI) and Metabolism-Lipophilicity Efficiency (MLE) are introduced for each fragment. The findings are applied to a Topliss style analysis that focuses on metabolic stability.

Novel 3D-descriptors using Triplets Of Pharmacophoric Points (TOPP) were evaluated in QSAR-studies on 80 apoptosis-inducing 4-aryl-4 H -chromenes. A predictive QSAR model was obtained using PLS, confirmed by means of internal and external validations. Performance of the TOPP approach was compared with that of other 2D- and 3D-descriptors; statistical analysis indicates that TOPP descriptors perform best. A ranking of TOPP > GRIND > BCI 4096 = ECFP > FCFP > GRID-GOLPE ≫ DRAGON ⋙ MDL 166 was achieved. Finally, in a ‘consensus’ analysis predictions obtained using the single methods were compared with an average approach using six out of eight methods. The use of the average is statistically superior to the single methods. Beyond it, the use of several methods can help to easily investigate the presence/absence of outliers according to the ‘consensus’ of the predicted values: agreement among all the methods indicates a precise prediction, whereas large differences between predicted values (for the same compounds by different methods) would demand caution when using such predictions.