A combination of factors might however account

A combination of factors might, however, account for the low rate of success of drug development for neuropsychiatric disorders. First, in most neuropsychiatric disorders, the symptomatology is heterogeneous and the neurobiology remains poorly understood, making therapeutic targets difficult to identify. This calls for better biomarkers and careful stratification of the patients enrolled in the clinical trials. Second, while rodent models are key for basic science research and the testing of early disease-related hypotheses, it is likely that there is an overreliance on rodent models in drug discovery at later preclinical stages of drug development, particularly for neuropsychiatric disorders. Additional attention must be paid to species differences both in terms of receptor function and differences in neuronal networks. A notable example of where significant limitations can exist when using rodents to model human conditions is exemplified in the recent studies of Yin et al., 2017 [9]. Whereas, humans with 15q13.3 microdeletion syndrome, caused by C646 deletions involving the CHRNA7 gene, manifest behavioral deficits (e.g., cognitive impairments, deficits in social interactions) often observed in neuropsychiatric conditions such as Autism and schizophrenia, Chrna7 knockout in mice does not recapitulate this spectrum of neurobehavioral phenotypes. Moreover, while it is common for ADMET (absorption, distribution, metabolism, excretion, and toxicity) studies to include higher animals (e.g., dogs, nonhuman primates), most of the efficacy assessments are made in rodent models, a particular limitation since the behavioral repertoire of rodents is quite limited compared to humans. Even when higher animal species are utilized for AD-related studies, it is rare for evaluations to be made in older, more translationally relevant subjects. In addition, as it is discussed subsequently, in many cases the preclinical studies focus on acute and/or short term dose-effect evaluations whereas in the clinical trials, they tend to be longer [6], see Reconciling experiments and drug exposure time. As in the case of “Alice” growing and shrinking in the different situations of her path and highlighting the distortion of the world, a clearer view of the experimental conditions is required in preclinical studies to predict success in clinical trials. As discussed by the Hatter in the Lewis Carroll fantasy book, “I see what I eat” is not the same as “I eat what I see”. This easily translates in the world of drug discovery as: short-term experiments in animals with a high drug concentration are not the same as long-term exposures with a low concentration in humans (Table 3). Second, from the clinical trial side, there is a host of factors that may also contribute to the so-called “treatment failures”. There is a mindset (likely in part, driven by regulatory agencies) that large number of subjects (Ns) increase the statistical power of the trial to detect group differences. These pressures lead sponsors to enlist multiple study sites (in some cases across several countries) which make quality control a significant challenge. This is particularly important given the reliance on observational rating scales for drug efficacy assessments in Alzheimer’s disease where human errors and biases can make it difficult to differentiate true efficacy problems from apparent failures due to Type II errors [7], [10]. It could be argued that focusing on a smaller number of better characterized patients might yield stronger effects. Interestingly, results from a recent study on the efficacy of the α7 specific compound ABT-126 in schizophrenia confirmed positive results only in non-smokers which forced the clinical trial to a halt due to the small size of the population [11]. In smaller cohorts, the reliability and skills of the patient evaluators can be better assured; patient ratings on cognitive assessments can be repeated and averaged for statistical reliability, and an adequate number of subjects can be enrolled who do not suffer from coexisting diseases that require the use of other prescribed medications. These factors can confound dosing, efficacy, and side effect assessments of the test compound. The smaller, better-controlled studies would be more efficient and economical, and they could be streamlined to provide more rapid feedback to the preclinical scientists who could in turn suggest study modifications and new drug development directions. Here it is also important to note that studies with a very small number of patients may be useful for identifying additional disorders that might benefit from nAChR ligands (e.g., Autism Spectrum Disorders, certain forms of epilepsy) [12], [13]. Third, a further complexity observed in clinical trials is the reduced compound to placebo effect. This may be attributable to the insufficient initial characterization of the patient cohort and the relevance of the clinical care to cognitive performance. Improvements in cognitive performance can result from the increases in human interaction and the medical attention in both placebo- and drug treated patients. Consequently, successful clinical trials may require a more thorough assessment of study subjects before drug/placebo treatments are initiated.