Computers in pharmaceutical research and development
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The document provides an overview of the evolution of computers in pharmaceutical research and development from the 1940s to the 1990s, highlighting key milestones in computational chemistry and the integration of models in drug discovery processes. It discusses the transition from academic to industrial applications of computing in chemistry, the importance of quantitative structure-activity relationships (QSAR), and the role of new technologies in enhancing drug development efficiency. It concludes by addressing the challenges and advancements related to statistical modeling in pharmaceutical research.
Computers in pharmaceutical research and development
- 1. COMPUTERS IN PHARMACEUTICAL RESEARCH AND DEVELOPMENT PRESENTED BY SUJITHA MARY MPHARM ST JOSEPH COLLEGE OF PHARMACY 1
- 2. HISTORY OF COMPUTERS IN PHARMACEUTICAL RESEARCH AND DEVELOPMENT -INTRODUCTION Today, computers are so ubiquitous in pharmaceutical research and development that it may be hard to imagine a time when there were no computers to assist the medicinal chemist or biologist. Computers began to be utilized at pharmaceutical companies as early as the 1940s. There were several scientific and engineering advances that made possible a computational approach to design and develop a molecule 2
- 3. One fundamental concept understood by chemists was that chemical structure is related to molecular properties including biological activity. Hence if one could predict properties by calculations, one might be able to predict which structures should be investigated in the laboratory. Another fundamental, well-established concept was that a drug would exert its biological activity by binding to and/or inhibiting some biomolecule in the body. ( This concept stems from Fischer’s famous lock-and-key hypothesis ) Pioneering research in the 1950s attacked the problem of linking electronic structure and biological activity. A good part of this work was collected in the 1963 book by Bernard and Alberte Pullman of Paris, France, which fired the imagination of what might be possible with calculations on biomolecules . 3
- 4. The earliest papers that attempted to mathematically relate chemical structure and biological activity were published in Scotland in the middle of the nineteenth century . This work and a couple of other papers were forerunners(pecursor) to modern quantitative structureactivity relationships (QSAR). The early computers were designed for military and accounting applications, but gradually it became apparent that computers would have a vast number of uses. 4
- 5. COMPUTATIONAL CHEMISTRY: THE BEGINNINGS AT LILLY In the late 1950s or early 1960s, the first computers to have stored programs of scientific interest were acquired. One of these was an IBM 650; it had a rotating magnetic drum memory consisting of 2000 accessible registers. The programs, the data input, and the output were all in the form of IBM punched cards. It was carried out by Lilly’s research statistics group under Dr. Edgar King. It was not until 1968, when Don Boyd joined the second theoretical chemist in the group, that the computers at Lilly started to reach a level of size, speed, and sophistication to be able to handle some of the computational requirements of various evaluation and design efforts. Don brought with him Hoffmann’s EHT program from Harvard and Cornell. 5
- 6. GERMINATION: THE 1960s in 1960 essentially 100% of the computational chemists were in academia, not industry. The students coming from those academic laboratories constituted the main pool of candidates that industry could hire for their initial ventures into using computers for drug discovery. Another pool of chemists educated using computers were X-ray crystallographers. One of the largest computers then in use by theoretical chemists and crystallographers was the IBM 7094. Support staff operated the tape readers, card readers, and printers. 6
- 7. Programs were written in FORTRAN II. Programs used by the chemists usually ranged from half a box to several boxes long. Carrying several boxes of cards to the computer center was good for physical fitness. If a box was dropped or if a card reader mangled some of the cards, the tedious task of restoring the deck and replacing the torn cards ensued. Finally in regard to software, we note one program that came from the realm of crystallography. That program was ORTEP (Oak Ridge Thermal Ellipsoid Program), which was the first widely used program for (noninteractive) molecular graphics . 7
- 8. GAINING A FOOTHOLD: THE 1970s Lilly management of the 1970s standed by further permanent growth. It was not until near the end of the 1980s that Lilly resumed growing its computational chemistry group to catch up to the other large pharmaceutical companies. Other companies such as Merck and Smith Kline and French (using the old name) entered the field a few years later. Unlike Lilly, they hired chemists trained in organic chemistry and computers. Widely used models included members of the IBM 360 and 370 series. Placing these more powerful machines in-house made it easier and more secure to submit jobs and retrieve output. But output was still in the form of long printouts. 8
- 9. Computational chemists in the pharmaceutical industry also expanded from their academic upbringing by acquiring an interest in force field methods, QSAR, and statistics. To solve research problems in industry, one had to use the best available technique, and this did not mean going to a larger basis set or a higher level of quantum mechanical theory. It meant using molecular mechanics or QSAR. The 1970s were full of small successes such as finding correlations between calculated and experimental properties. Some of these correlations were published. Even something so grand as the de novo design of a pharmaceutical was attempted but was somewhat beyond reach. Two new computer-based resources were launched in the 1970s. One was the Cambridge Structural Database (CSD) 9
- 10. GROWTH: THE 1980s If the 1960s were the Dark Ages and the 1970s were the Middle Ages, the 1980s were the Renaissance, the Baroque Period, and the Enlightenment all rolled into one. The decade of the 1980s was when the various approaches of quantum chemistry, molecular mechanics, molecular simulations, QSAR, and molecular graphics coalesced into modern computational chemistry. Several exciting technical advances fostered the improved environment for computer use at pharmaceutical companies in the 1980s. The first was a development of the VAX 11/780 computer by Digital Equipment Corporation (DEC) in 1979. 10
- 11. FRUITION: THE 1990s The 1990s was a decade of fruition because the computer- based drug discovery work of the 1980s yielded an impressive number of new chemical entities reaching the pharmaceutical marketplace. Pharmaceutical companies were accustomed to supporting their own research and making large investments in it. supercomputers that were creating excitement at a small number of pharmaceutical companies, another hardware development was attracting attention at just about every company interested in designing drugs. Workstations from Silicon Graphics Inc. (SGI) were becoming increasingly popular for molecular research 11
- 12. During tha time the Apple Macintoshes were well liked by scientists. However, in 1994 Apple lost its lawsuit against Microsoft regarding the similarities of the Windows graphical user interface (GUI) to Apple’s desktop design. QSAR proved to be one of the best approaches to providing assistance to the medicinal chemist in the 1990s. Therefore, computational chemistry experts play an important role in maximizing the potential benefits of computer based technologies. 12
- 13. STATISTICAL MODELING IN PHARMACEUTICAL RESEARCH AND DEVELOPMENT The new major challenge that the pharmaceutical industry is facing in the discovery and development of new drugs is to reduce costs and time needed from discovery to market, while at the same time raising standards of quality. In parallel to this growing challenge, technologies are also dramatically evolving, opening doors to opportunities never seen before. Some of the best examples of new technologies available in the life sciences are microarray technologies or high-throughput- screening. 13
- 14. The new technologies have been integrated to do the same things as before, but faster, deeper, smaller, with more automation, with more precision, and by collecting more data per experimental unit. However, the standard way to plan experiments, to handle new results, to make decisions has remained more or less unchanged, except that the volume of data, and the disk space required to store it, has exploded exponentially. This standard way to discover new drugs is essentially by trial and error. 14
- 15. the process of discovery and development of new drugs has been drawn to highlight the pivotal role that models (simplifi ed mathematical descriptions of real-life mechanisms) play in many R&D activities. In some areas of pharmaceutical research, like pharmacokinetics/pharmacodynamics (PK/PD), models are built to characterize the kinetics and action of new compounds or platforms of compounds, knowledge crucial for designing new experiments and optimizing drug dosage. Models are also developed in other areas, as for example in medicinal chemistry with QSAR-related models. These can all be defined as mechanistic models, and they are useful. 15
- 16. On the other side, many models of a different type are currently used in the biological sciences: Using empirical models, universally applicable, whose basic purpose is to appropriately represent the noise, but not the biology or the chemistry, statisticians give whenever possible a denoised picture of the results, so that field scientists can gain better understanding and take more informed decisions. The dividing line between empirical models and mechanistic models is not as clear and obvious as some would pretend. Mechanistic models are usually based on chemical or biological knowledge, or the understanding we have of chemistry or biology. 16
- 17. Today, however, the combination of mathematics, statistics, and computing allows us to effectively use more and more complex mechanistic models directly incorporating our biological or chemical knowledge. 17
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