The plasma concentration-time profile of a drug is essential to explain the relationship between the administered dose and the kinetics of drug action. However, in some cases such as in pre-clinical pharmacology or phase-III clinical studies where it is not always possible to collect all the required PK information, this relationship can be difficult to establish. In these circumstances several authors have proposed simple models that can analyse and simulate the kinetics of the drug action in the absence of PK data. The present work further develops and evaluates the performance of such an approach. A virtual compartment representing the biophase in which the concentration is in equilibrium with the observed effect is used to extract the (pharmaco)kinetic component from the pharmacodynamic data alone. Parameters of this model are the elimination rate constant from the virtual compartment (KDE), which describes the equilibrium between the rate of dose administration and the observed effect, and the second parameter, named EDK(50) which is the apparent in vivo potency of the drug at steady state, analogous to the product of EC(50), the pharmacodynamic potency, and clearance, the PK "potency" at steady state. Using population simulation and subsequent (blinded) analysis to evaluate this approach, it is demonstrated that the proposed model usually performs well and can be used for predictive simulations in drug development. However, there are several important limitations to this approach. For example, the investigated doses should extend from those producing responses well below the EC(50) to those producing ones close to the maximum response, optimally reach steady state response and followed until the response returns to baseline. It is shown that large inter-individual variability on PK-PD parameters will produce biases as well as large imprecision on parameter estimates. It is also clear that extrapolations to dosage routes or schedules other than those used to estimate the parameters should be undertaken with great caution (e.g., in case of non-linearity or complex drug distribution). Consequently, it is advised to apply this approach only when the underlying structural PD and PK are well understood. In any case, K-PD model should definitively not be substituted for the gold standard PK-PD model when correct full model can and should be identified.

Monsoon rainfall over South Asia has decreased during the last 5 to 6 decades according to several sets of observations. Although sea surface temperature (SST) has risen across the Indo-Pacific warm pool during this period, the expected accompanying increased rainfall has occurred only in the tropical western Pacific. The above changes noted in observations are also seen in a coupled climate model, but only when the model includes the recent increase in greenhouse gas concentration. The hypothesis that the robust rise in SST over the warm pool, perhaps anchored by an increase in greenhouse gas concentrations, is instrumental in the east–west shift in monsoon rainfall (enhanced rainfall over tropical western Pacific and decreased rainfall over South Asia) is proposed. A suite of controlled experiments with an atmospheric general circulation model has been performed to isolate the impact of regional SST warming trends on the dryness over South Asia. Model experiments support the hypothesis that the rising SST trend over the tropical western Pacific has changed the atmospheric circulation: over the Bay of Bengal more dry and cool air is advected from the northeast than previously. Moist static energy budget diagnostics on the model solutions identify the sources for this east–west shift. SST warming over the warm pool has accelerated in recent decades. Therefore, a close monitoring of that warming is important for long-term variations of monsoon rainfall. The inconsistency in the amplitude of drying over South Asia among the various land-based rainfall observations and lack of sustained rainfall observations over the open oceans, however, poses constraints in the results.

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In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.