technologies

NEW CHEMICAL ENTITY SCREENING PLATFORM
Platform Background
Nectid has developed a screening platform that utilizes existing knowledge- scientific art, patent databases, published clinical trial information, published bioassays, protein expressions to
  1. design new chemical entities with better clinical profiles than known drugs, and to
  2. discover newer use for known molecular entities.
The initial goal is to conceptualize, design and synthesize NCEs that improve the clinical profile of currently approved drugs by several orders of magnitude. The platform is disease specific, often mechanism specific, and uses thousands of training sets and corresponding biological responses and requires customization depending on the therapeutic area.

Nectid's proprietary software developed using disease specific biological responses and correlating them with molecular structures of hundreds of known bioactive compounds. The platform uses a sophisticated mathematical function to provide a screening tool to predict an active compound from hundreds of newly designed chemical entities. We are continuing to improve the platform by two approaches- 1) continual addition of training molecules and new biological response relevant to that specific training molecule and 2) building a complementary molecular profile model discussed in detail below.

Our Antidepressant screening platform is exemplified below for illustration.
Background
The "monoamine hypothesis" of depression, which involves imbalances in serotonergic, noradrenergic and possibly dopaminergic functions, has dominated notions and explanations of the pathophysiology of depression since the empirical discovery of the antidepressant properties of monoamine oxidase inhibitors (MAOIs) and tricyclics. Although the monoaminergic neurotransmitters (5-HT, NA and dopamine, DA) are undoubtedly involved, it is now recognized that changes in the levels of monoamines produced by antidepressants and subsequent adaptive processes, in particular a change in the sensitivity of some of their receptors, are not sufficient on their own to explain the mechanism of action of antidepressants.

There are four non-monoamine mechanisms been evaluated for novel antidepressants-1) neurokinin 1, 2) corticotropin-releasing factor 1 receptors, 3) NMDA (N-methyl-d-aspartate) receptor and 4) a more innovative melatonin pathways. Nectid’s current efforts are directed to develop NCEs that modulates SSRI-5HT1A (Monoamine) and melatonin mechanisms.

ASP - The Antidepressant Screening Platform (ASP) was developed using training set of 600 monoamine modulators (tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs) molecules), over 500 known melatonin, NMDA modulating compounds, The ASP module customized from our generic platform included different sets of training molecules-
  1. Arylpiperazine derivatives; Buspirone, Gepirone, Tandospirone, Ipsapirone, 1-(2-Methoxyphenyl)-4-(4-phthalimidobutyl)piperazine (NAN-190), 4-[3-(1-benzotriazolyl)propyl]-1-(2-methoxyphenyl)piperazine (MP 3022), Alnespirone, Lesopitron, vilazodone and two carbon molecules-BMY-7378 and Flesinoxan and WAY-100635.
  2. Melatonin derivatives-Melatonin, Agomelatine, Ramelteon, Tasimelteon, PD-6735 (6-chloro-(beta)-methyl melatonin) etc and their structural variations from the published art.
  3. NMDA modulators - Amantadine, Dextromethorphan Memantine and related compounds
The data sets included several hundreds of molecules that have piperazine/aryl piperazine, melatonin and NMDA modulating molecular features. Nectid software researches the public databases for structure and activity relationship of hundreds of published compounds. The whole molecules reported to be active in the published art and have bioassays constitute the basic training set. The basic training set is used to create a platform elements by stripping each of the molecules into functional groups- Amino, Aminoacyl, cyano, four-carbon, three –carbon, indole etc groups and each assigned an activity value and weight derived from an algorithm. This constitutes the three dimensional matrix of structural features, the corresponding activities and assigned weights F= (x, y, z). This matrix and algorithm constitutes the ASP screening platform. When a new molecule is screened for activity, the platform first reduces a new molecule into a set of structural features that are found in the three dimensional matrix. It then integrates and infers value, from the three dimensional matrix for each structural feature of the new compound- for example- Cyano, Acyl Carbonyl group or Indole ring. Once individual structural features are evaluated, it integrates and infers value for the entire molecule. Most of the currently known efforts to identify molecular similarities use two dimensional systems or pattern recognition.
Multiple Mechanism Platform
The program's additional goal is focused on modeling, designing and validating NCEs that would modulate two pathways- 1)"monoamine" pathway and 2) a second non-monoaminergic mechanism of action. The strategy was to explore the possibility of designing an NCE that incorporates molecular features that, if possible synergistically, affect higher level cellular and behavioral responses from two different mechanisms that accompany depression. We believe the mechanism of action of SSRI-5HT1A and melatonin significantly differentiates from them from other antidepressants. This is particularly true for agomelatine. The program envisages achieving this by optimized modulation of four targets-5-HT1A Binding, 5-HT Reuptake Inhibition, D2 Binding, MT1 and MT2 receptor (combinatorial ligand design) modulation. This goal is likely to be achieved by optimizing a complex combination of modulations- 5-HT Reuptake/5-HT1A, (5-HTreuptake/ 5-HT1A)/D2, [(5-HTreuptake/ 5-HT1A)/D2] to {MT1/MT2} correlations.
Molecular/Disease Profile Platform
Nectid is currently working to significantly enhance the above module by incorporating two separate and parallel platforms comprising-
  1. Molecular Features and Activities and
  2. Activities and Diseases
Each subgroup software modules separately research literature and published clinical trials results. These two modules help in building molecules >>> pharmacophore/structural features >>> activities >>> diseases units. The goal of these modules is to construct a disease matrix of structural/pharmacopore features, activities and weights help addressing whether disparate structural/pharmacophore features could be bought together to design a better NCE for a specific disease or whether one specific pharmacophore/structural feature be deployed for designing an NCE for a different disease. The results from Molecular Profile Platform are being integrated to ASP to build a robust screening mechanism.

We utilize public domain Omics sources and literature mining for identifying specific molecular features and consecutively diseases associated with specific pharmacopore. There are certain pharmacophores that modulate several diseases- For Example Aryl Piperazines modulate pathways related- malaria, benign prostatic hyperplasia and an array of CNS disorders. First a set of molecular features (molecular footprint) are derived by combining results from pharmacopore-specific literature mining and profiling data sets specifically aimed at reflecting the impact of pharmacopore on the level of activity/protein coding gene expression. Based on this molecular pharmacopore footprint, a set of diseases are delineated by computing significant enrichment of disease terms associated to publications being linked to these features, again utilizing literature extraction methods. This resulting set of diseases is thus indirectly related to pharmacopore via molecular features being targets or otherwise affected by pharmacopore. Additionally a second disease list is derived by mining information available for published clinical trials utilizing pharmacopore in treatment. Finally both disease lists, namely on the one hand derived on the basis of the molecular pharmacopore footprint, and on the other hand already evaluated in clinical trials were compared for identifying the overlap of diseases for validating the veracity of our approach, but also to identify potential further diseases which, at least from the viewpoint of molecular profiles, might be addressable by pharmacopore. This data set also helps in developing a third three dimensional matrix (training set) involving molecular features, activity and disease profile.