Why is Aged Acetylcholinesterase So Difficult to Reactivate?

Organophosphorus agents are potent inhibitors of acetylcholinesterase. Inhibition involves successive chemical events. The first is phosphylation of the active site serine to produce a neutral adduct, which is a close structural analog of the acylation transition state. This adduct is unreactive toward spontaneous hydrolysis, but in many cases can be reactivated by nucleophilic medicinal agents, such as oximes. However, the initial phosphylation reaction may be followed by a dealkylation reaction of the incipient adduct. This reaction is called aging and produces an anionic phosphyl adduct with acetylcholinesterase that is refractory to reactivation. This review considers why the anionic aged adduct is unreactive toward nucleophiles. An alternate approach is to realkylate the aged adduct, which would render the adduct reactivatable with oxime nucleophiles. However, this approach confronts a considerable-and perhaps intractable-challenge: the aged adduct is a close analog of the deacylation transition state. Consequently, the evolutionary mechanisms that have led to transition state stabilization in acetylcholinesterase catalysis are discussed herein, as are the challenges that they present to reactivation of aged acetylcholinesterase.

Development of quantitative structure activity relationships for the binding affinity of methoxypyridinium cations for human acetylcholinesterase.

Among the most toxic substances known are the organophosphorus (OP) compounds used as pesticides and chemical warfare agents. Owing to their high toxicity there is a number of efforts underway to develop effective therapies for OP agent exposure. To date all therapies in use treat inhibited acetylcholinesterase (AChE), but are ineffective for the treatment of inhibited AChE, which has undergone a subsequent hydrolysis process, referred to as aging. Toward developing a therapy for treating victims of OP intoxication in the aged state we have developed Quantitative Structure-Activity Relationships (QSARs) based on the AM1 semiempirical quantum mechanical method using the program, CODESSA (COmprehensive Descriptors for Structural and Statistical Analysis). Using this methodology we obtained a multiple correlation QSAR equation which gave R(2)=0.9359 for a random training set of 38 ligands and R(2)=0.9236 for prediction on a random test set of 9 ligands.