1. Introduction
Pharmacogenomics experts have recognized that genomics-based approaches to drug discovery appear to suffer from some sort of information overload problem
(A. D. Roses, Burns, Chissoe, Middleton, & Jean, 2005, p. 179). More specifically, the explosion of human genomics information may have been outpaced by a concurrent explosion of noise within that data, leading to a significant attrition rate in the pharmaceutical pipeline (A. D. Roses et al., 2005, p. 179). However, it is not entirely clear how the concepts of information overload and signal-to-noise apply to information-based struggles in pharmacogenomics. In order to improve our understanding of the barriers to optimal use of pharmacogenomics information for drug discovery purposes we must first briefly unpack competing ideas about information overload and signal-to-noise and then contextualize the appropriate ideas within PGx-based drug discovery (henceforth PGx-DD).

2. Explaining Too Much Information in PGx-based Drug Discovery: Information Theory or Information Overload?

Genomics research pioneer and GSK Senior VP for Genomics Research Allen Roses has recently shed light on why pharmacogenomics-based approaches may not be optimal. According to Roses, who arguably is in a unique position to understand the problem, the central problem is one arising from information struggles. Roses writes,

What factors have limited target selection and drug discovery productivity? Although HTS technologies were successfully implemented and spectacular advances in mining chemical space have been made, the universe for selecting targets expanded, and in turn almost exploded with an inundation of information. Perhaps the best explanation for the initial modest success observed was the dramatic increase in the ‘noise-to-signal’ ratio, which led to a rise in the rate of attrition at considerable expense. The difficulty in making the translation from the identification of all genes to selecting specific disease-relevant targets for drug discovery was not realistically appreciated (A. D. Roses et al., 2005, p. 179).

What Roses calls the “noise-to-signal” ratio sounds like the problem of information overload, yet it also sounds as if it borrows from the language of Information Theory as put forth by Claude Shannon. Roses’ insight seems to corroborate Sean Ekins’ observation that already-extant data is not optimally utilized (2005). Pharmacogenomics is failing to deliver because PGx researchers and organizations utilizing PGx research have been unable to meet the information challenges concomitant with the explosion of data.

The language Allen Roses uses to describe struggles with information in the field of PGx-based drug discovery refers both to a signal-to-noise ratio and to information overload. The terminology appears, however, to be rather ambiguously utilized in the context of PGx-DD. “Noise-to-signal” seems to refer to Claude Shannon’s mathematical theory of communication (Shannon & Weaver, 1949) while the problems described by PGx professionals sound more like cognitive issues related to more formal notions of information overload.

2.1.Shannon’s Mathematical Theory of Communication
In 1948, Claude Shannon of Bell Labs completed work on his mathematical theory of communication. For so doing, Shannon is credited as fathering the field of Information Theory. It is from Shannon’s theory that the notion of signal-to-noise arises, among many other concepts crucial to any understanding of information. In his introduction to the ensuing book publication comprising Shannon’s work on the theory, Warren Weaver explains that the theory was supposed to deal with three distinct levels of communications problems, as follows:

Level A. How accurately can the symbols of communication be transmitted? (The technical problem.)

Level B. How precisely do the transmitted symbols convey the desired meaning? (The semantic problem.)

Level C. How effectively does the received meaning affect conduct in the desired way? (The effectiveness problem.) (Shannon & Weaver, 1949, p. 4)

Information in Shannon’s sense is not used in the ordinary sense of information. While by ‘information’ we ordinarily mean something akin to that which has already been said/written, Shannon means information in the sense of what may possibly be said (Shannon & Weaver, 1949, p. 8). For Shannon, information is a probable message sent over a channel (e.g., a telephone wire) and his concern is with describing general properties of the transmission and interpretation of such electronic signals.

Concerns about the ratio of signal-to-noise with respect to information transmission do originate from Shannon’s own communication theory work. The very ratio of signal-to-noise appears in Shannon’s theoretical examination of channel capacity with power limitation (Shannon & Weaver, 1949, p. 100). Shannon uses the ratio of the power source of the signal (denoted as P) to the power of the noise (denoted as N) in order to provide a general way of calculating how many bits per second any communication pathway can actually transmit. Shannon replaces P with S, the peak allowed transmitter power, in order to adjust channel capacity where peak power limits the rate of the channel to transmit bits. According to Shannon the upper bound rate of a channel is the channel band times the log of the ratio of signal plus noise to noise where the signal-to-noise ratio is low (Shannon & Weaver, 1949, p. 107). Loosely speaking, the rate at which telephone wires, coaxial cables, wireless networks, and the like can transmit messages varies logarithmically with the ratio of peak power (signal) to background noise on the channel (noise).

Shannon & Weaver’s specified problem set does not accurately match the sort of problem a drug discovery researcher is facing, not at least without a considerable stretch. Shannon’s sense of information in his definitive work on communication theory does not seem quite the same as the sort of information we are dealing with when we speak of genomics research data. Finally, Shannon’s notion of signal-to-noise can at best only loosely apply to notions of researchers struggling with too much information in their hands. Shannon is writing about communication channels, not people.

Efforts Shannon may have made to model specifically human communication in his theoretical work appears to be at best tertiary to the central thrust of his work, which was to generalize the properties of electronic communications systems. In short, Information Theory as proffered by Shannon does not appear to apply in any straightforward way to the sort of “noise-to-signal” problem Allen Roses describes or any other human communication problems that can occur independently of electronic signals. The signal-to-noise problem Roses reports is an information problem to be sure but it appears to be an information problem unlikely to be either explained or resolved through the lens of Shannon’s communication theory.

2.2.Information Overload
The concept of the possibility of too much information dates back to ancient times (Bawden, Holtham, & Courtney, 1999, p. 249). The recurring concern of information overload stems from the general notion that a person’s work becomes inefficient from increasing difficulty experienced in locating the best pieces of information. With the advent of computer-based information retrieval systems in the 1950s (Bawden et al., 1999, p. 249) as well as the beginnings of the mass proliferation of scientific research literature (Ziman, 1980), the concern became more frequently and more directly articulated and investigated. While any exact definition of information overload is elusive issues of relevance and efficiency are commonly notes as are issues of both data management and psychic strain (Bawden et al., 1999, p. 250). The constant problem however is that information overload stands for a struggle—a struggle that increases as a collection of information grows beyond human tractability. The recurring solution inevitably takes the form of methods or techniques that allow a person to locate some tractable set of pieces of information of sufficient quality in a reasonable amount of time in order to aid the person in completing the task.

3. Impact of information overload on PGx-based drug discovery
Information overload describes the general problem of “noise-to-signal” referred to by Allen Roses. Roses characterizes the information problem facing PGx-DD as having increased the rate of attrition of drug candidates in the pharmaceutical pipeline. Further, he states that the solution to the problem is an increase in “specific, disease-relevant targets” relative to all genomic data (A. D. Roses et al., 2005, p. 179). In other words, the proliferation of genomic data has drowned out this highly specific disease-relevant genomic information to the point that it increases drug discovery failure. The way to resolve the issue is to reduce information overload in PGx-DD by restricting the flow of information to PGx researchers to highly specific disease-relevant genomic information. As Roses says, providing researchers with validating evidence is crucial.

4. Validating evidence, novelty, and a PGx-info quality model
What, however, frames, delimits, or describes validating evidence for candidate targets? Roses states that disease-specific targets chosen based on well-trod beliefs “have a significant probability of being the totally wrong target” (A. D. Roses et al., 2005, p. 180). It is therefore not enough to identify highly specific disease relevant data efficiently; the data must support infrequent or entirely novel theories. The data must in essence have the characteristic of supporting novelty, of supporting ideas not commonly held, of bolstering theories that appear to be unreasonable.

The quality of PGx information should be evaluated using the following three criteria:

(a) the disease-relevance of the information,

(b) the specificity of the information, and

(c) the novelty of the information or the novelty of the theory supported by the information.


Bawden, D., Holtham, C., & Courtney, N. (1999). Perspectives on information overload. Aslib Proceedings, 51(8), 249-255.

Ekins, S., Bugrim, A., Nikolsky, Y., & Nikolskaya, T. (2005). Systems biology: Applications in drug discovery. In S. C. Gad (Ed.), Drug discovery handbook (pp. 123-183). Hoboken, New Jersey: Wiley Interscience.

Roses, A. D., Burns, D. K., Chissoe, S., Middleton, L., & Jean, P. S. (2005). Disease-specific target selection: A critical first step down the right road. Drug Discovery Today, 10(3), 177-189.

Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana and Chicago: University of Illinois Press.

Ziman, J. M. (1980). The proliferation of scientific literature – a natural process. Science, 208(4442), 369-371.

(NOTE: the preceding document is a revised expert from my master’s thesis.)