The Lilly results of 11 July 2012 are not surprising, considering that the main ingredient of LY2140023 is LY404039, which is both a glutamate agonist and a weak partial dopamine agonist with only one-hundredth the potency of aripiprazole (Seeman and Guan, 2009; Seeman, 2012a), and considering that closer inspection of the clinical data (Kinon et al., 2011) showed that olanzapine was effective in schizophrenia, while LY2140023 was not (Seeman, 2012b).

References:

Kinon BJ, Zhang L, Millen BA, Osuntokun OO, Williams JE, Kollack-Walker S, Jackson K, Kryzhanovskaya L, Jarkova N, . A multicenter, inpatient, phase 2, double-blind, placebo-controlled dose-ranging study of LY2140023 monohydrate in patients with DSM-IV schizophrenia. J Clin Psychopharmacol . 2011 Jun ; 31(3):349-55. Abstract

Seeman P, Guan HC. Glutamate agonist LY404,039 for treating schizophrenia has affinity for the dopamine D2(High) receptor. Synapse. 2009 Oct ; 63(10):935-9. Abstract

Seeman P. An agonist at glutamate and dopamine D2 receptors, LY404039. Neuropharmacology. 2012a Jul 4. Abstract

Seeman P. Comment on "A multicenter, inpatient, phase 2, double-blind, placebo-controlled dose-ranging study of LY2140023 monohydrate in patients with DSM-IV schizophrenia" by Kinon et al. J Clin Psychopharmacol. 2012b Apr ; 32(2):291-2; author reply 292-293. Abstract

View all comments by Philip Seeman

This is indeed another setback for the schizophrenia patient community, and it underscores the difficulty of translating animal model outcomes to the clinical situation. We have to think about introducing a new technology in schizophrenia drug discovery and development that would combine the best of preclinical animal information, but transplanted into a humanized environment to reverse this string of clinical failures.

One such approach is Quantitative Systems or Network Pharmacology, a computer-based mechanistic disease model of biophysically realistic neuronal networks that combines preclinical neurophysiology with human pathology, and clinical and imaging data (the topic of a recent NIH White Paper). Such an approach can be calibrated with retrospective clinical data, and then used to predict and examine future clinical trials. Applying this quantitative paradigm to the (also much publicized) failure of Dimebon in AD, researchers found that there was a fundamental off-target effect that precluded Dimebon from having cognitive benefits. Further analyses suggested that...  Read more

This is indeed another setback for the schizophrenia patient community, and it underscores the difficulty of translating animal model outcomes to the clinical situation. We have to think about introducing a new technology in schizophrenia drug discovery and development that would combine the best of preclinical animal information, but transplanted into a humanized environment to reverse this string of clinical failures.

One such approach is Quantitative Systems or Network Pharmacology, a computer-based mechanistic disease model of biophysically realistic neuronal networks that combines preclinical neurophysiology with human pathology, and clinical and imaging data (the topic of a recent NIH White Paper). Such an approach can be calibrated with retrospective clinical data, and then used to predict and examine future clinical trials. Applying this quantitative paradigm to the (also much publicized) failure of Dimebon in AD, researchers found that there was a fundamental off-target effect that precluded Dimebon from having cognitive benefits. Further analyses suggested that an imbalance in a common dopaminergic phenotype could increase part of the clinical signal difference as observed in the first (successful) Phase 2 trial.

In the case of schizophrenia, we find that affecting glutamatergic (such as with the mGluR2/R3 agonist) or GABA neurotransmission almost always leads to an inverse U-shaped dose response, because of the intrinsic balance between excitation and inhibition in cortical networks. Using such an approach forces discovery scientists to look beyond the single target and think about the impact on networks and circuits that ultimately drive human behavior and pathology in CNS disorders.

Unlike the traditional, currently used "cartoon"-based qualitative drawings, this approach allows for a quantitative outcome that, in principle, can help define the optimal "sweet spot" of the dose response by looking at the outcome of endophenotypes such as BOLD fMRI.

References:

Athan Spiros, Hugo Geerts. 2012. A quantitative way to estimate clinical off-target effects for human membrane brain targets in CNS Research and Development. Exp Pharmacology, 4; 53-61.

Athan Spiros, Patrick Roberts, Hugo Geerts. (2012) A Quantitative Systems Pharmacology Computer Model for Schizophrenia Efficacy and Extrapyramidal Side Effects, Drug Dev. Res, 73(4): 1098-1109.