The present contribution describes how in silico models and methods are applied at different stages of the drug discovery process in the pharmaceutical industry. A description of the most relevant computational methods and tools is given along with an evaluation of their performance in the assessment of potential genotoxic impurities and the prediction of off-target in vitro pharmacology. The challenges of predicting the outcome of highly complex in vivo studies are discussed followed by considerations on how novel ways to manage, store, exchange, and analyze data may advance knowledge and facilitate modeling efforts. In this context, the current status of broad data sharing initiatives, namely, eTOX and eTransafe, will be described along with related projects that could significantly reduce the use of animals in drug discovery in the future.

Pre-competitive data sharing can offer the pharmaceutical industry significant benefits in terms of reducing the time and costs involved in getting a new drug to market through more informed testing strategies and knowledge gained by pooling data. If sufficient data is shared and can be co-analyzed, then it can also offer the potential for reduced animal usage and improvements in the in silico prediction of toxicological effects. Data sharing benefits can be further enhanced by applying the FAIR Guiding Principles, reducing time spent curating, transforming and aggregating datasets and allowing more time for data mining and analysis. We hope to facilitate data sharing by other organizations and initiatives by describing lessons learned as part of the Enhancing TRANslational SAFEty Assessment through Integrative Knowledge Management (eTRANSAFE) project, an Innovative Medicines Initiative (IMI) partnership which aims to integrate publicly available data sources with proprietary preclinical and clinical data donated by pharmaceutical organizations. Methods to foster trust and overcome non-technical barriers to data sharing such as legal and IPR (intellectual property rights) are described, including the security requirements that pharmaceutical organizations generally expect to be met. We share the consensus achieved among pharmaceutical partners on decision criteria to be included in internal clearance pro­cedures used to decide if data can be shared. We also report on the consensus achieved on specific data fields to be excluded from sharing for sensitive preclinical safety and pharmacology data that could otherwise not be shared.

Adenosine contributes to the pathophysiology of respiratory disease, and adenosine challenge leads to bronchospasm and dyspnoea in patients. The equilibrative nucleoside transporter 1 (ENT1) terminates the action of adenosine by removal from the extracellular environment. Therefore, it is proposed that inhibition of ENT1 in respiratory disease patients leads to increased adenosine concentrations, triggering bronchospasm and dyspnoea. This study aims to assess the translation of in vitro ENT1 inhibition to the clinical incidence of bronchospasm and dyspnoea in respiratory disease, cardiovascular disease and healthy volunteer populations. Four marketed drugs with ENT1 activity were assessed; dipyridamole, ticagrelor, draflazine, cilostazol. For each patient population, the relationship between in vitro ENT1 [3H]-NBTI binding affinity (Ki) and [3H]-adenosine uptake (IC50) to the incidence of: (1) bronchospasm/severe dyspnoea; (2) tolerated dyspnoea and; (3) no adverse effects, was evaluated. A high degree of ENT1 inhibition (≥13.3x Ki, ≥4x IC50) associated with increased incidence of bronchospasm/severe dyspnoea for patients with respiratory disease only, whereas a lower degree of ENT1 inhibition (≥0.1x Ki, ≥0.05x IC50) associated with a tolerable level of dyspnoea in both respiratory and cardiovascular disease patients. ENT1 inhibition had no effect in healthy volunteers. Furthermore, physicochemical properties correlative with ENT1 binding were assessed using a set of 1625 diverse molecules. Binding to ENT1 was relatively promiscuous (22% compounds Ki<1μM) especially for neutral or basic molecules, and greater incidence tracked with higher lipophilicity (clogP >5). This study rationalises inclusion of an assessment of ENT1 activity during early safety profiling for programs targeting respiratory disorders.

Although many preclinical programs in central nervous system research and development intend to develop highly selective and potent molecules directed at the primary target, they often act upon other off-target receptors. The simple rule of taking the ratios of affinities for the candidate drug at the different receptors is flawed since the affinity of the endogenous ligand for that off-target receptor or drug exposure is not taken into account. We have developed a mathematical receptor competition model that takes into account the competition between active drug moiety and the endogenous neurotransmitter to better assess the off-target effects on postsynaptic receptor activation under the correct target exposure conditions. As an example, we investigate the possible functional effects of the weak off-target effects for dopamine-1 receptor (D1R) in a computer simulation of a dopaminergic cortical synapse that is calibrated using published fast-cyclic rodent voltammetry and human imaging data in subjects with different catechol-O-methyltransferase genotypes. We identify the conditions under which off-target effects at the D1R can lead to clinically detectable consequences on cognitive tests, such as the N-back working memory test. We also demonstrate that certain concentrations of dimebolin (Dimebon), a recently tested Alzheimer drug, can affect D1R activation resulting in clinically detectable cognitive decrease. This approach can be extended to other receptor systems and can improve the selection of clinical candidate compounds by potentially dialing-out harmful off-target effects or dialing-in beneficial off-target effects in a quantitative and controlled way.