Introduction

Abstract

2.1 Introduction

The low probability of finding useful compounds in random plant screening programmes (approx. one plant sample in 10000 will show promising activity of interest to

researchers), particularly in areas of high biodiversity, is one reason why private drug companies are reluctant to engage in bioprospecting de novo (Soejarto, 1993;

Macilwain, 1998). Methods to streamline and/or optimise the selection of organisms are therefore essential.

The annotated checklist of medicinal and magical plants in southern Africa (Arnold

et al.,

2002), is a near-comprehensive ethnomedicinal plant-use data set for the region (Grace and Crouch, 2003). One of the key applications to which this data can be put is in the identification of candidate plants for novel drug development from the regional flora. The use of regression analyses (Figure 2.1)(after Moerman, 1979) is a simple yet effective means of reducing copious ethnomedicinal taxa to a small group likely to yield effective bioactivities. Such a reduction is achieved by grouping plant taxa by order or family and then applying a regression analysis to identify outliers. The occurrence of outliers falsifies the null hypothesis, which states that plant-use by traditional peoples is

completely random. This implies that the percentage of taxa selected by ethnomedicinal practitioners for ethnomedicinal purposes from different plant orders would approach parity. Outliers above the regression line represent taxonomic groups that are targeted by ethnomedicinal practitioners and as such, should be earmarked for further

investigation or prioritisation in bioprospecting. Such orders and families will be referred to as 'hot'. Outlying orders that occur below the regression line are used most

infrequently by ethnomedicinal practitioners. The method of prioritising key taxonomic groups presented here is desirable for drug bioprospecting programmes due to improved efficiency.

Once key taxa (primary candidates) have been identified, the plant selection process may be further refined by incorporating chemotaxonomic and/or natural product data. It has been reported that plant secondary metabolites are often specific to taxonomic groups (Hegnauer, 1967; Cronquist, 1980), and close relatives of the primary candidates

may display similar pharmacological activities. The inclusion of related taxa can either be undertaken before initial bioassays or after primary candidate assessment. Molecular trees based on rbd.... DNA provide a useful framework for assessing the comparative merits of secondary compound classes as chemotaxonomic characters (Grayer

et al.,

1999), and so plant families and orders (excluding the Pteridophyta) in this analysis were grouped according to recently published phylogenetic trees (Sowe

et al.,

2000; Chaw

et al.,

2000; APG 11, 2003).

Regional analysis of ethnomedicinal plants in southern Africa is instructive for several reasons, and should influence the planning and execution of drug bioprospecting. For example, biogeographic, habitat and habit information may be scrutinized in a similar way to yield a greater number of promising plant taxa. In addition, the historical settlement patterns and subsequent distribution of indigenous peoples and later migrants in the region may have significantly shaped the current body of recorded traditional plant-use knowledge (as reflected in the SANSI MedList database). This factor could well skew the results of any regression analyses. Similarly, the loss of historical data influences the number of current ethnomedicinal taxa recorded for that region (perhaps through cultural attrition). The presence of botanical hot spots and areas of high endemism should also be noted (Cowling and Hilton-Taylor, 1994), particularly if the goal is to include as many indigenous/endemic plants in a drug bioprospecting programme as possible, for either political, economic or conservation reasons. The patchy distribution and scarcity of many endemic taxa will have resulted in reduced contact with ethnomedicinal practitioners, which may skew results of the regression analyses in terms of both numbers and geographic region. It could be argued that botanical hotspots are under increasing threat due to habitation destruction and these areas should be regarded as priorities for bioprospecting ventures.

1

List all ethnomedicinal andregional taxa for

regression analysis

1

Regroup all taxa according to APG 11 (2003)phylogenetic system

1

Align ethnomedicinal and regional orders Regression analysis

1

Apply a least squares regression

analysis tothe ethnomedicinal taxa

(groupedbyorder) vs. regional taxa (grouped by order).

calculate residual values: subtract predicted number of ethnomedicinal taxa ---+ per order from

actual number of ethnomedicinal taxa

per order I

Taxa in positive outlying orders are prioritised for

bioprospecting .

3 ..

Isolate outliers:

residual values greater or less than

the population variance (cut-off) aredeemedoutliers

Repeatthe regression analysis using taxa groupedby family instead of

byorder

Remove outlying orders and repeat

the regression analysis

Identify families withhighest residualvalues in

the outlying orders.

1

Taxainoutlying orders/families are prioritised for

bioprospectlng •

Figure 2.1 OvelView of the use of least squares regression analyses for the prioritisation of plant taxa

Etkin (1986) noted that plant selection by ethnomedicinal practitioners may be patterned in accordance with the belief that certain attributes (e.g. leaf shape or colour) serve to indicate utility. This concept is generally referred to as the Doctrine of Signatures in which a plant is considered desirable due to the presence of a physical property that resembles some characteristic associated with the disease of concern. Analysis of ethnomedicinal taxa in the hot families should therefore be performed, with a view to identifying the occurrence of similar plant organ characteristics in utilised taxa. The results may also help to better direct the conservation and sustainable use of plants being harvested, either for traditional or pharmaceutical preparations.

A review of the general phytochemistry of ethnomedicinal plant families may prove beneficial in assessing correlations between the documented pharmacological activities of taxa in those families and their ethnomedicinal use. Records which document the occurrence of pharmacologically active secondary metabolites within monophyletic assemblages will be of particular interest as this may lead to the identification of related taxa with similar efficacies. This due to secondary metabolites (which are generally most active against disease-causing organisms) being considered valuable for taxonomic purposes (Cronquist, 1980). Toxicity of lower or higher ethnomedicinal taxa should also be investigated. A direct comparison of the current results with those presented by Moerman (1991) was not undertaken, due to the very different floras present in the two regions (North America and southern Africa).