Antigiardial activity of novel guanidine compounds

From four focused compound libraries based on the known anticoccidial agent robenidine, 44 compounds total were synthesised and screened for antigiardial activity. All active compounds were counter-screened for antibiotic and cytotoxic action. Of the analogues examined, 21 displayed IC50<5 μM, seven with IC50<1.0 μM. Most active were 2,2′-bis{[4-(trifluoromethoxy)phenyl]methylene}carbonimidic dihydrazide hydrochloride (30), 2,2′-bis{[4-(trifluoromethylsulfanyl)phenyl]methylene}carbonimidic dihydrazide hydrochloride (32), and 2,2′-bis[(2-bromo-4,5-dimethoxyphenyl)methylene]carbonimidic dihydrazide hydrochloride (41) with IC50=0.2 μM. The maximal observed activity was a 5 h IC50 value of 0.2 μM for 41. The clinically used metronidazole was inactive at this timepoint at a concentration of 25 μM. Robenidine off-target effects at bacteria and cell line toxicity were removed. Analogue 41 was well tolerated in mice treated orally (100 mg/kg). Following 5 h treatment with 41, no Giardia regrowth was noted after 48 h

Discovery of 4,6-bis(2-((E)-benzylidene)hydrazinyl)pyrimidin-2-Amine with antibiotic activity

Robenidine (E)-N'-((E)-1-(4-chlorophenyl)ethylidene)-2-(1-(4-chlorophenyl)ethylidene)hydrazine-1-carboximidhydrazide displays methicillin-resistant Staphyoccoccus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) MICs of 2 μg mL-1. Herein we describe the structure-activity relationship development of a novel series of guanidine to 2-aminopyrimidine isosteres that ameliorate the low levels of mammalian cytotoxicity in the lead compound while retaining good antibiotic activity. Removal of the 2-NH2 pyrimidine moiety renders these analogues inactive. Introduction of a central 2-NH2 triazine moiety saw a 10-fold activity reduction. Phenyl to cyclohexyl isosteres were inactive. The 4-BrPh and 4-CH3Ph with MIC values of 2 and 4 μg mL-1, against MRSA and VRE respectively, are promising candidates for future development.Cecilia C. Russe, Andrew Stevens, Kelly A. Young, Jennifer R. Baker ... Manouchehr Khazandi ... Abiodun Ogunniyi ... et al

Acquisition of Object-Centred Domain Models from Planning Examples

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits the assumption that actions change the state of objects, and require objects to be in a certain state before they can be executed. The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences. In this paper we describe the implemented LOCM algorithm, and analyse its performance by its application to the induction of domain models for several domains. To evaluate the algorithm, we used random action sequences from existing models of domains, as well as solutions to past IPC problems

Automated acquisition of action knowledge

AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a $partial$ domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient. To do this we extend GIPO's so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning. We illustrate the algorithm as part of an overall method to acquire a planning domain model, and detail results that show the efficacy of the induced model

Importing ontological information into planning domain models

We investigate an approach to alleviating the problems of knowledge engineering for AI Planning by importing object structures and object behaviours from shared knowledge structures. In this paper we describe our first steps at devising a method to import knowledge from an application ontology into a form usable within a planning domain model. We evaluate an implemented tool called OWL2OCL which assists in the translation of ontological information into a form usable by a planner

An evaluation of Opmaker2

Opmaker2 is a knowledge acquisition and formulation tool, which inputs a domain ontology and a training sequence, and outputs a set of PDDL operator schema. This paper evaluates Opmaker2 (a) by comparing it against GIPO's object life history editor tool (b) by analysis of its method and its robustness to choice of training sequence

Action Knowledge Acquisition with Opmaker2

AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a partial domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient. To do this we extend GIPO’s Opmaker system [1] so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning. We illustrate the algorithm as part of an overall method to acquire a planning domain model, and detail results that show the efficacy of the induced model

Gram-positive and gram-negative antibiotic activity of asymmetric and monomeric robenidine analogues

Desymmetrisation of robenidine 1, N',2-bis((E)-4-chlorobenzylidene)hydrazine-1-carboximidhydrazide, and the introduction of imine alkyl substituents gave good antibiotic activity. Of note was the increased potency of 17 and 20 against VRE with 20 the most active, MIC of 0.5 μg mL-1. Analogues 2, 14, 17, 19 and 20 were equipotent or more potent than the lead 1. Introduction of an indole moiety, 30, resulted in the most MRSA active robenidine analogue, MIC of 1.0 μg mL-1. Imine C=NH isosteres (C=O / C=S) were inactive. Monomeric analogues, 33-35 were 16 - 64 μg mL-1 active against MRSA and VRE. Analogue 36, lacking the terminal hydrazide NH moiety showed modest Gram-negative activity at 64 μg mL-1. 4-t-Butyl 45 was Gram -positive and -negative active at of 16 - 64 μg mL-1. Typically additional aromatic moiety modification was poorly tolerated, except with concomitant introduction of an imine C-alkyl moiety. The activity of these analogues against MRSA and VRE ranged from 8 μg mL-1 with 64 and 68, to inactive (MIC > 128 μg mL-1) with the naphthyl 69 and 70 and the indole 73. Gram-negative activity was most promising with 62 and 68 at 16 μg mL-1 against E. coli. Against Ps. aeruginosa, the highest activity observed was with MIC values of 32 μg mL-1 with 62 and 64. Combined, these findings support the further development of the (E)-2-benzylidenehydrazine-1-carboximidamide scaffold as a promising Gram-positive and Gram-negative antibiotic development.Cecilia C. Russell, Andrew Stevens, Hongfei Pi, Manouchehr Khazandi, Abiodun D. Ogunniyi, Kelly A. Young, Jennifer R. Baker, Siobhann N. McCluskey, Stephen W. Page, Darren J. Trott and Adam McCluske