The Medicinal Chemistry of 5-HT6 Receptor Ligands with a Focus on Arylsulfonyltryptamine Analogs

Arylsulfonyl analogs of aminopyrimidines (e.g. Ro 04-6790; 2), aminopyridines (e.g. Ro 63-0563; 3), 1-phenylpiperazines (e.g. SB-271046; 4), and tryptamines (e.g. MS-245; 5) were described as the first examples of selective 5-HT6 receptor antagonists only ten years ago. Today, hundreds of compounds of seemingly diverse structure have been reported. The early antagonists featured an arylsulfonyl group leading to the widespread assumption that an arylsulfonyl moiety might be critical for binding and antagonist action. With respect to the arylsulfonyltryptamines, it seems that neither the “arylsulfonyl” nor the “tryptamine” portion of these compounds is essential for binding or for antagonist action, and some such derivatives even display agonist action. The present review describes many of the currently available 5-HT6 receptor ligands and, unlike prior reviews, provides a narrative of the thinking (where possible) that led to their design, synthesis, and evaluation. The arylsulfonyltryptamines are also used as the structural basis of attempts to relate various structure-types to one another to afford a better understanding of the overall structural requirements for 5-HT6 receptor binding

Markov chain model to study the occurrence of pre-monsoon thunderstorms over Bhubaneswar, India

The present work deals with pre-monsoon thunderstorms over Bhubaneswar belonging to the state of Orissa, India. A Markovian approach has been adopted to discern the probabilistic behavior of the time series of the occurrence and non-occurrence of this hazardous weather event by introducing a dichotomy within the time series. After a painstaking analysis through chi-square tests, we have identified serial independence in a few years and first-order two-state Markovian dependence in a few years (2000, 2001, 2004 and 2006). Finally, for the years of first-order two-state Markovian dependence, it has been observed that the probability of occurrence or non-occurrence of thunderstorm gets higher if the state of the previous day is similar to that of the current day. Furthermore, the probability of getting non-thunderstorm day followed by non-thunderstorm day is higher than the probability of getting thunderstorm day followed by thunderstorm day. It has been also observed that the unconditional climatological probability of the occurrence of severe pre-monsoon thunderstorm implied by the Markov chain is closely in agreement with the observed relative frequencies. However, it could be revealed that Markov chain cannot, in general, be suggested as a predictive tool for pre-monsoon thunderstorms under study without investigating the serial dependence inherent in the time series